Search results for: aerial images

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

Procedia PDF Downloads 123

Authors: Tahir Ali Akbar, Hirra Jabbar

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Land cover change analysis plays a significant role in understanding the trends of urban sprawl and land use transformation due to anthropogenic activities. In this study, the spatio-temporal dynamics of major land covers were analyzed in the last twenty years (1988-2016) for District Lahore located in the Punjab Province of Pakistan. The Landsat satellite imageries were downloaded from USGS Global Visualization Viewer of Earth Resources Observation and Science Center located in Sioux Falls, South Dakota USA. The imageries included: (i) Landsat TM-5 for 1988 and 2001; and (ii) Landsat-8 OLI for 2016. The raw digital numbers of Landsat-5 images were converted into spectral radiance and then planetary reflectance. The digital numbers of Landsat-8 image were directly converted into planetary reflectance. The normalized difference vegetation index (NDVI) was used to classify the processed images into six major classes of water, buit-up, barren land, shrub and grassland, sparse vegetation and dense vegetation. The NDVI output results were improved by visual interpretation using high-resolution satellite imageries. The results indicated that the built-up areas were increased to 21% in 2016 from 10% in 1988. The decrease in % areas was found in case of water, barren land and shrub & grassland. There were improvements in percentage of areas for the vegetation. The increasing trend of urban sprawl for Lahore requires implementation of GIS based spatial planning, monitoring and management system for its sustainable development.

Keywords: land cover changes, NDVI, remote sensing, urban sprawl

Procedia PDF Downloads 315

Authors: Zhi Zhu, Boquan Zhang, Tian Jing, Jingjing Li, Tao Wang

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Data assimilation is a model and data hybrid-driven method that dynamically fuses new observation data with a numerical model to iteratively approach the real system state. It is widely used in state prediction and parameter inference of continuous systems. Because of the discrete event system’s non-linearity and non-Gaussianity, traditional Kalman Filter based on linear and Gaussian assumptions cannot perform data assimilation for such systems, so particle filter has gradually become a technical approach for discrete event simulation data assimilation. Hence, we proposed a particle filter-based discrete event simulation data assimilation method and took the unmanned aerial vehicle (UAV) maintenance service system as a proof of concept to conduct simulation experiments. The experimental results showed that the filtered state data is closer to the real state of the system, which verifies the effectiveness of the proposed method. This research can provide a reference framework for the data assimilation process of other complex nonlinear systems, such as discrete-time and agent simulation.

Keywords: discrete event simulation, data assimilation, particle filter, model and data-driven

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Authors: Nada Bezić, Valerija Dunkić, Antonija Markovina, Mirko Rušćić

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Valeriana is a genus of the well-known medicinal plant of Valerianacea family and growing wild in the sub-Mediterranean area. This abstract reports the types and distribution of trichomes and phyto-active composition of the essential oil of the Valeriana tuberosa from mountain Kozjak, near Split, Croatia. Two types of glandular trichomes: peltate (one basal epidermal cell, one short stalk cell and a small head) and capitate trichomes (one basal epidermal cell, one elongated stalk cell) were observed on leaf, using light microscopy. We analyzed the composition of the essential oil of stems and leaves of V. tuberosa species. Water distilled essential oils from aerial parts of investigation plant have been analysed by GC and GC/MS using VF-5ms capillary column. The total yield of oil was 0.2%, based on dry weight of samples. Forty compounds representing 94.1% of the total oil of V. tuberosa. This essential oil was characterized by a high concentration of isovaleric acid (17.2%), geranyl isovalerate (12.2%) and caryophyllene oxide (7.7%). The present study gives additional knowledge about micromorphological traits and secondary metabolites contents on the genus Valeriana.

Keywords: essential oil, isovaleric acid, Valeriana tuberosa, Croatia

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Authors: Alessandro Preziosi, Antonio Prioletti, Luca Castangia

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Deformable part models achieve high precision in pedestrian recognition, but all publicly available implementations are too slow for real-time applications. We implemented a deformable part model algorithm fast enough for real-time use by exploiting information about the camera position and orientation. This implementation is both faster and more precise than alternative DPM implementations. These results are obtained by computing convolutions in the frequency domain and using lookup tables to speed up feature computation. This approach is almost an order of magnitude faster than the reference DPM implementation, with no loss in precision. Knowing the position of the camera with respect to horizon it is also possible prune many hypotheses based on their size and location. The range of acceptable sizes and positions is set by looking at the statistical distribution of bounding boxes in labelled images. With this approach it is not needed to compute the entire feature pyramid: for example higher resolution features are only needed near the horizon. This results in an increase in mean average precision of 5% and an increase in speed by a factor of two. Furthermore, to reduce misdetections involving small pedestrians near the horizon, input images are supersampled near the horizon. Supersampling the image at 1.5 times the original scale, results in an increase in precision of about 4%. The implementation was tested against the public KITTI dataset, obtaining an 8% improvement in mean average precision over the best performing DPM-based method. By allowing for a small loss in precision computational time can be easily brought down to our target of 100ms per image, reaching a solution that is faster and still more precise than all publicly available DPM implementations.

Keywords: autonomous vehicles, deformable part model, dpm, pedestrian detection, real time

Procedia PDF Downloads 276

Authors: A. U. Haque, W. Asrar, A. A Omar, E. Sulaeman, J. S. M. Ali

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Buoyancy force applied on deformable symmetric bodies can be estimated by using Archimedes Principle. Such bodies like ellipsoidal bodies have high volume to surface ratio and are isometrically scaled for mass, length, area and volume to follow square cube law. For scaling up such bodies, it is worthwhile to find out the scaling relationship between the other physical quantities that represent thermodynamic, structural and inertial response etc. So, dimensionless similarities to find an allometric scale can be developed by using Bukingham π theorem which utilizes physical dimensions of important parameters. Base on this fact, physical dependencies of buoyancy system are reviewed to find the set of physical variables for deformable bodies of revolution filled with expandable gas like helium. Due to change in atmospheric conditions, this gas changes its volume and this change can effect the stability of elongated bodies on the ground as well as in te air. Special emphasis was given on the existing similarity parameters which can be used in the design of experiments of such bodies whose shape is affected by the external force like a drag, surface tension and kinetic loads acting on the surface. All these similarity criteria are based on non-dimensionalization, which also needs to be consider for scaling up such bodies.

Keywords: Bukhigham pi theorem, similitude, scaling, buoyancy

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Authors: U. Pruthviraj, Praveen Kumar R. A. K. Athul, K. V. Gangadharan, S. Rao Shrikantha

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An open source based autonomous unmanned marine surface vehicle (UMSV) is developed for some of the marine applications such as pollution control, environmental monitoring and thermal imaging. A double rotomoulded hull boat is deployed which is rugged, tough, quick to deploy and moves faster. It is suitable for environmental monitoring, and it is designed for easy maintenance. A 2HP electric outboard marine motor is used which is powered by a lithium-ion battery and can also be charged from a solar charger. All connections are completely waterproof to IP67 ratings. In full throttle speed, the marine motor is capable of up to 7 kmph. The motor is integrated with an open source based controller using cortex M4F for adjusting the direction of the motor. This UMSV can be operated by three modes: semi-autonomous, manual and fully automated. One of the channels of a 2.4GHz radio link 8 channel transmitter is used for toggling between different modes of the USMV. In this electric outboard marine motor an on board GPS system has been fitted to find the range and GPS positioning. The entire system can be assembled in the field in less than 10 minutes. A Flir Lepton thermal camera core, is integrated with a 64-bit quad-core Linux based open source processor, facilitating real-time capturing of thermal images and the results are stored in a micro SD card which is a data storage device for the system. The thermal camera is interfaced to an open source processor through SPI protocol. These thermal images are used for finding oil spills and to look for people who are drowning at low visibility during the night time. A Real Time clock (RTC) module is attached with the battery to provide the date and time of thermal images captured. For the live video feed, a 900MHz long range video transmitter and receiver is setup by which from a higher power output a longer range of 40miles has been achieved. A Multi-parameter probe is used to measure the following parameters: conductivity, salinity, resistivity, density, dissolved oxygen content, ORP (Oxidation-Reduction Potential), pH level, temperature, water level and pressure (absolute).The maximum pressure it can withstand 160 psi, up to 100m. This work represents a field demonstration of an open source based autonomous navigation system for a marine surface vehicle.

Keywords: open source, autonomous navigation, environmental monitoring, UMSV, outboard motor, multi-parameter probe

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Authors: N. Behidj, K. Benyounes, T. Dahmane, A. Allem

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The aim of this study was to investigate the antimicrobial activity of flavonoids isolated from the aerial part of a medicinal plant which is Thymus inodorusby the middle agar diffusion method on following microorganisms. We have Staphylococcus aureus, Escherichia coli, Pseudomonas fluorescens, AspergillusNiger, Aspergillus fumigatus and Candida albicans. During this study, flavonoids extracted by stripping with steam are performed. The yields of flavonoids is 7.242% for the aqueous extract and 28.86% for butanol extract, 29.875% for the extract of ethyl acetate and 22.9% for the extract of di - ethyl. The evaluation of the antibacterial effect shows that the diameter of the zone of inhibition varies from one microorganism to another. The operation values obtained show that the bacterial strain P fluoresces, and 3 yeasts and molds; A. Niger, A. fumigatus and C. albicansare the most resistant. But it is noted that, S. aureus is shown more sensitive to crude extracts, the stock solution and the various dilutions. Finally for the minimum inhibitory concentration is estimated only with the crude extract of Thymus inodorus flavonoid.Indeed, these extracts inhibit the growth of Gram + bacteria at a concentration varying between 0.5% and 1%. While for bacteria to Gram -, it is limited to a concentration of 0.5%.

Keywords: antimicrobial activity, organic extracts, aqueous extracts, Thymus numidicus

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Authors: Yufeng Li, Yinan Ma, Jing Wu, Chengnian Long

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Infrared object detection aims at separating small and dim target from clutter background and its capabilities extend beyond the limits of visible light, making it invaluable in a wide range of applications such as improving safety, security, efficiency, and functionality. However, existing methods are usually sensitive to the noise of the input infrared image, leading to a decrease in target detection accuracy and an increase in the false alarm rate in high-noise environments. To address this issue, an infrared small target detection algorithm called YOLO-IR is proposed in this paper to improve the robustness to high infrared noise. To address the problem that high noise significantly reduces the clarity and reliability of target features in infrared images, we design a soft-threshold coordinate attention mechanism to improve the model’s ability to extract target features and its robustness to noise. Since the noise may overwhelm the local details of the target, resulting in the loss of small target features during depth down-sampling, we propose a deep and shallow feature fusion neck to improve the detection accuracy. In addition, because the generalized Intersection over Union (IoU)-based loss functions may be sensitive to noise and lead to unstable training in high-noise environments, we introduce a Wasserstein-distance based loss function to improve the training of the model. The experimental results show that YOLO-IR achieves a 5.0% improvement in recall and a 6.6% improvement in F1-score over existing state-of-art model.

Keywords: infrared small target detection, high noise, robustness, soft-threshold coordinate attention, feature fusion

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Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

Procedia PDF Downloads 179

Authors: Yolanda Havenga

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Being a competent nurse requires clinical, general, and moral competencies. Moral competence is a culmination of moral perceptions, moral judgment, moral behaviour, and moral identity. Moral identity is the values, images, and fundamental principles held in the collective minds and memories of nurses about what it means to be a ‘good nurse’. It is important to explore and describe South African nurses’ moral identities and excavate the post-colonial counter-narrative to nurses moral identities as a better understanding of these identities will enable means to positively address nurses’ moral behaviours. This study explored the moral identity of nurses within the South African context. A qualitative approach was followed triangulating with phenomenological and narrative designs with the same purposively sampled group of professional nurses. In-depth interviews were conducted until saturation of data occurred about the sampled nurses lived experiences of being a nurse in South Africa. They were probed about their core personal-, social-, and professional values. Data were analysed based on the steps used by Colaizzi. These nurses were then asked to write a narrative telling a personal story that portrayed a significant time in their professional career that defines their identity as a nurse. This data were analysed using a critical narrative approach and findings of the two sets of data were merged. Ethical approval was obtained and approval from all relevant gate keepers. In the findings, themes emerged related to personal, social and professional values, images and fundamental principles of being a nurse within the South African context. The findings of this study will inform a future national study including a representative sample of South African nurses.

Keywords: moral behaviour, moral identity, nurses, qualitative research

Procedia PDF Downloads 279

Authors: Zhongmin Wang, Wudong Fan, Hengshan Zhang, Yimin Zhou

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In data-driven prognostic methods, the prediction accuracy of the estimation for remaining useful life of bearings mainly depends on the performance of health indicators, which are usually fused some statistical features extracted from vibrating signals. However, the existing health indicators have the following two drawbacks: (1) The differnet ranges of the statistical features have the different contributions to construct the health indicators, the expert knowledge is required to extract the features. (2) When convolutional neural networks are utilized to tackle time-frequency features of signals, the time-series of signals are not considered. To overcome these drawbacks, in this study, the method combining convolutional neural network with gated recurrent unit is proposed to extract the time-frequency image features. The extracted features are utilized to construct health indicator and predict remaining useful life of bearings. First, original signals are converted into time-frequency images by using continuous wavelet transform so as to form the original feature sets. Second, with convolutional and pooling layers of convolutional neural networks, the most sensitive features of time-frequency images are selected from the original feature sets. Finally, these selected features are fed into the gated recurrent unit to construct the health indicator. The results state that the proposed method shows the enhance performance than the related studies which have used the same bearing dataset provided by PRONOSTIA.

Keywords: continuous wavelet transform, convolution neural net-work, gated recurrent unit, health indicators, remaining useful life

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Authors: F. Z. Aliyat, B. Ben Messaoud, L. Nassiri, E. Bouiamrine, J. Ibijbijen

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The present study aims to assess the biological nitrogen fixation in the soybean tested in different Moroccan soils combined with the rhizobial inoculation. These effects were evaluated by the plant growth mainly by the aerial biomass production, total nitrogen content and the proportion of the nitrogen fixed. This assessment clearly shows that the inoculation with bacteria increases the growth of soybean. Five different soils and a control (peat) were used. The rhizobial inoculation was performed by applying the peat that contained a mixture of 2 strains Sinorhizobium fredii HH103 and Bradyrhizobium. The biomass, the total nitrogen content and the proportion of nitrogen fixed were evaluated under different treatments. The essay was realized at the greenhouse the Faculty of Sciences, Moulay Ismail University. The soybean has shown a great response for the parameters assessed. Moreover, the best response was reported by the inoculated plants compared to non- inoculated and to the absolute control. Finally, good production and the best biological nitrogen fixation present an important ecological technology to improve the sustainable production of soybean and to ensure the increase of the fertility of soils.

Keywords: biological nitrogen fixation, inoculation, rhizobium, soybean

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Authors: Silvija Šiljeg, Ivan Marić, Ante Šiljeg

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The accessibility of urban green spaces (UGS) is an integral element in the quality of life. Due to rapid urbanization, UGS studies have become a key element in urban planning. The potential benefits of space for its inhabitants are frequently analysed. A functional transport network system and the optimal spatial distribution of urban green surfaces are the prerequisites for maintaining the environmental equilibrium of the urban landscape. An accessibility analysis was conducted as part of the Urban Green Belts Project (UGB). The development of a GIS database for Zadar was the first step in generating the UGS accessibility indicator. Data were collected using the supervised classification method of multispectral LANDSAT images and manual vectorization of digital orthophoto images (DOF). An analysis of UGS accessibility according to the ANGst standard was conducted in the first phase of research. The accessibility indicator was generated on the basis of seven objective measurements, which included average UGS surface per capita and accessibility according to six functional levels of green surfaces. The generated indicator was compared with subjective measurements obtained by conducting a survey (718 respondents) within statistical units. The collected data reflected individual assessments and subjective evaluations of UGS accessibility. This study highlighted the importance of using objective and subjective measures in the process of understanding the accessibility of urban green surfaces. It may be concluded that when evaluating UGS accessibility, residents emphasize the immediate residential environment, ignoring higher UGS functional levels. It was also concluded that large areas of UGS within a city do not necessarily generate similar satisfaction with accessibility. The heterogeneity of output results may serve as guidelines for the further development of a functional UGS city network.

Keywords: urban green spaces (UGS), accessibility indicator, subjective and objective measurements, Zadar

Procedia PDF Downloads 252

Authors: Keerti Kembhavi, M. R. Archana, V. Anjaneyappa

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Most asphalt pavement condition evaluation use rating frameworks in which asphalt pavement distress is estimated by type, extent, and severity. Rating is carried out by the pavement condition rating (PCR), which is tedious and expensive. This paper presents the development of a low-cost technique for image pavement distress analysis that permits the identification of pothole and cracks. The paper explores the application of image processing tools for the detection of potholes and cracks. Longitudinal cracking and pothole are detected using Fuzzy-C- Means (FCM) and proceeded with the Spectral Theory algorithm. The framework comprises three phases, including image acquisition, processing, and extraction of features. A digital camera (Gopro) with the holder is used to capture pavement distress images on a moving vehicle. FCM classifier and Spectral Theory algorithms are used to compute features and classify the longitudinal cracking and pothole. The Matlab2016Ra Image preparing tool kit utilizes performance analysis to identify the viability of pavement distress on selected urban stretches of Bengaluru city, India. The outcomes of image evaluation with the utilization semi-computerized image handling framework represented the features of longitudinal crack and pothole with an accuracy of about 80%. Further, the detected images are validated with the actual dimensions, and it is seen that dimension variability is about 0.46. The linear regression model y=1.171x-0.155 is obtained using the existing and experimental / image processing area. The R2 correlation square obtained from the best fit line is 0.807, which is considered in the linear regression model to be ‘large positive linear association’.

Keywords: crack detection, pothole detection, spectral clustering, fuzzy-c-means

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Authors: Bensaid A., Mostephaoui T., Nedjai R.

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Nowadays, wetlands are the subject of contradictory debates opposing scientific, political and administrative meanings. Indeed, given their multiple services (drinking water, irrigation, hydrological regulation, mineral, plant and animal resources...), wetlands concentrate many socio-economic and biodiversity issues. In some regions, they can cover vast areas (>100 thousand ha) of the landscape, such as the Camargue area in the south of France, inside the Rhone delta. The high biological productivity of wetlands, the strong natural selection pressures and the diversity of aquatic environments have produced many species of plants and animals that are found nowhere else. These environments are tremendous carbon sinks and biodiversity reserves depending on their age, composition and surrounding environmental conditions, wetlands play an important role in global climate projections. Covering more than 3% of the earth's surface, wetlands have experienced since the beginning of the 1990s a tremendous revival of interest, which has resulted in the multiplication of inventories, scientific studies and management experiments. The geographical and physical characteristics of the wetlands of the central region conceal a large number of natural habitats that harbour a great biological diversity. These wetlands, one of the natural habitats, are still influenced by human activities, especially agriculture, which affects its layout and functioning. In this perspective, decision-makers need to delimit spatial objects (natural habitats) in a certain way to be able to take action. Thus, wetlands are no exception to this rule even if it seems to be a difficult exercise to delimit a type of environment as whose main characteristic is often to occupy the transition between aquatic and terrestrial environment. However, it is possible to map wetlands with databases, derived from the interpretation of photos and satellite images, such as the European database Corine Land cover, which allows quantifying and characterizing for each place the characteristic wetland types. Scientific studies have shown limitations when using high spatial resolution images (SPOT, Landsat, ASTER) for the identification and characterization of small wetlands (1 hectare). To address this limitation, it is important to note that these wetlands generally represent spatially complex features. Indeed, the use of very high spatial resolution images (>3m) is necessary to map small and large areas. However, with the recent evolution of artificial intelligence (AI) and deep learning methods for satellite image processing have shown a much better performance compared to traditional processing based only on pixel structures. Our research work is also based on spectral and textural analysis on THR images (Spot and IRC orthoimage) using two object-oriented approaches, the nearest neighbour approach (k-NN) and the Super Vector Machine approach (SVM). The k-NN approach gave good results for the delineation of wetlands (wet marshes and moors, ponds, artificial wetlands water body edges, ponds, mountain wetlands, river edges and brackish marshes) with a kappa index higher than 85%.

Keywords: land development, GIS, sand dunes, segmentation, remote sensing

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Authors: Gurukiran Veerapur, Nytik Birudavolu, Seetharam U. N., Chandravva Hebbi, R. Praneeth Reddy

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This work presents a robust approach for the recognition of characters in Telugu and Kannada, two South Indian scripts with structural similarities in characters. To recognize the characters exhaustive datasets are required, but there are only a few publicly available datasets. As a result, we decided to create a dataset for one language (source language),train the model with it, and then test it with the target language.Telugu is the target language in this work, whereas Kannada is the source language. The suggested method makes use of Canny edge features to increase character identification accuracy on pictures with noise and different lighting. A dataset of 45,150 images containing printed Kannada characters was created. The Nudi software was used to automatically generate printed Kannada characters with different writing styles and variations. Manual labelling was employed to ensure the accuracy of the character labels. The deep learning models like CNN (Convolutional Neural Network) and Visual Attention neural network (VAN) are used to experiment with the dataset. A Visual Attention neural network (VAN) architecture was adopted, incorporating additional channels for Canny edge features as the results obtained were good with this approach. The model's accuracy on the combined Telugu and Kannada test dataset was an outstanding 97.3%. Performance was better with Canny edge characteristics applied than with a model that solely used the original grayscale images. The accuracy of the model was found to be 80.11% for Telugu characters and 98.01% for Kannada words when it was tested with these languages. This model, which makes use of cutting-edge machine learning techniques, shows excellent accuracy when identifying and categorizing characters from these scripts.

Keywords: base characters, modifiers, guninthalu, aksharas, vattakshara, VAN

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Authors: Abdelrahman Elsehsah

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The main objective of this study is to assess changes in the water capacity of Aswan High Dam Lake (AHDL) caused by evaporation and seepage losses. To achieve this objective, a comprehensive methodology was employed. The methodology involves acquiring Sentinel-3 imagery and extracting the surface area of the lake using remote sensing techniques. Using water areas calculated from sentinel images, collected field data, and the lake’s water balance equation, monthly evaporation and seepage losses were estimated for the years 2021 and 2022. Based on the water balance method results, the average monthly evaporation losses for the year 2021 were estimated to be around 1.41 billion cubic meters (Bm3), which closely matches the estimates provided by the Ministry of Water Resources and Irrigation (MWRI) annual reports (approximately 1.37 Bm3 in the same year). This means that the water balance method slightly overestimated the monthly evaporation losses by about 2.92%. Similarly, the average monthly seepage losses for the year 2022 were estimated to be around 0.005 Bm3, while the MWRI reports indicated approximately 0.0046 Bm3. By another means, the water balance method overestimated the monthly seepage losses by about 8.70%. Furthermore, the study found that the average monthly evaporation rate within AHDL was 210.88 mm/month, which closely aligns with the computed value of approximately 204.9 mm/month by AHDA. These findings indicated that the applied water balance method, utilizing remote sensing and field data, is a reliable tool for estimating monthly evaporation and seepage losses as well as monthly evaporation rates in AHDL.

Keywords: Aswan high dam lake, remote sensing, water balance equation, seepage loss, evaporation loss

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Authors: Nadali Babaeian Jelodar, Chan Lai Keng, Arvind Bhatt, Laleh Bordbar, Leow E Shuen, Kamaruzaman Mohamed

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Recently artemisinin (the endoperoxide sesquiterpene lactone) has received considerable attention because of its antimalarial activity. It is isolated from the aerial part of the Artemisia annua L. Artemisinin is very difficult to synthesise also its production by mean of cell, tissue or organ cultures is very low. Presently, only its extraction from A. annua L. plants remains the only source of the drug. The reported yield of artemisinin from leaves of A. annua L. is very low and unstable, with yields typically less than 1% of leaf dry weight. To increase the percentage of artemisinin, researchers have been engaged in developing new varieties. A review concerning the breeding of A. annua L. is presented. The aim of this review is to bring together most of the available scientific research papers about the breeding conducted on the genus A. annua L., which is currently scattered across various publications. Through this review the authors hope to attract the attention of breeders throughout the world to focus on the unexplored potential of A. annua L. species. Also the future scope of this plant has been emphasized with a view of the importance of breeding of A. annua L. for increasing of artemisinin content. By releasing of new cultivar of A. annua L. and cultivation of this plant offers the opportunity to optimize yield and achieve a uniform, high quality product.

Keywords: Artemisia annua L., breeding, artemisinin, cultivation, medicinal plant

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Authors: Hachemi Nouria

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The olive tree is a Mediterranean tree, which belongs to the family Oleaceae. The Olea genus contains various species and subspecies, and the only species bearing edible fruit is Olea europaea. The desired issue in this study is to provide the current status of plant cover and especially the training in Olea europaea currently existing in the major centers of the region of Tlemcen. While based on the flora and biometric aspect of this plant germplasm. In order to make an assessment of the phytomass, we made measurements of the four parameters of the aerial part of the taxon: height, diameter, and canopy density to ten feet of the olive tree per station. The floristic analysis shows a certain floristic difference between the different stations. The vegetal formations reflect the biotic and abiotic conditions including climate affecting the ecosystem. Biometric study on the feet of Olea in the six study sites, has led us to conclude that the four measured parameters provides insight on the development or degradation of Olea feet depending on the layout of the stations and the factors environmental. We find that the terrains are havens for these assets. Also the local microclimate (Oued Thalweg) promotes the healthy development of this species.

Keywords: olivier, ecology, biometrics, Tlemcen, Algeria

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Authors: James Gallagher, Phillip Benachour

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As location aware mobile technologies become ever more omnipresent, the prospect of exploiting their context awareness to enforce learning approaches thrives. Utilizing the growing acceptance of ubiquitous computing, and the steady progress both in accuracy and battery usage of pervasive devices, we present a case study of remote location viewing, how the application can be utilized to support mobile learning in situ using an existing scenario. Through the case study we introduce a new innovative application: Mobipeek based around a request/response protocol for the viewing of a remote location and explore how this can apply both as part of a teacher lead activity and informal learning situations. The system developed allows a user to select a point on a map, and send a request. Users can attach messages alongside time and distance constraints. Users within the bounds of the request can respond with an image, and accompanying message, providing context to the response. This application can be used alongside a structured learning activity such as the use of mobile phone cameras outdoors as part of an interactive lesson. An example of a learning activity would be to collect photos in the wild about plants, vegetation, and foliage as part of a geography or environmental science lesson. Another example could be to take photos of architectural buildings and monuments as part of an architecture course. These images can be uploaded then displayed back in the classroom for students to share their experiences and compare their findings with their peers. This can help to fosters students’ active participation while helping students to understand lessons in a more interesting and effective way. Mobipeek could augment the student learning experience by providing further interaction with other peers in a remote location. The activity can be part of a wider study between schools in different areas of the country enabling the sharing and interaction between more participants. Remote location viewing can be used to access images in a specific location. The choice of location will depend on the activity and lesson. For example architectural buildings of a specific period can be shared between two or more cities. The augmentation of the learning experience can be manifested in the different contextual and cultural influences as well as the sharing of images from different locations. In addition to the implementation of Mobipeek, we strive to analyse this application, and a subset of other possible and further solutions targeted towards making learning more engaging. Consideration is given to the benefits of such a system, privacy concerns, and feasibility of widespread usage. We also propose elements of “gamification”, in an attempt to further the engagement derived from such a tool and encourage usage. We conclude by identifying limitations, both from a technical, and a mobile learning perspective.

Keywords: context aware, location aware, mobile learning, remote viewing

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Authors: Bryce R. Jolley, Michael Uchic

Abstract:

This study presents initial results toward the correlative characterization of inherent defects of Ti-6Al-4V additive manufacture (AM). X-Ray Computed Tomography (CT) defect data are compared and correlated with microscopic photographs obtained via automated serial sectioning. The metal AM specimen was manufactured out of Ti-6Al-4V virgin powder to specified dimensions. A post-contour was applied during the fabrication process with a speed of 1050 mm/s, power of 260 W, and a width of 140 µm. The specimen was stress relief heat-treated at 16°F for 3 hours. Microfocus CT imaging was accomplished on the specimen within a predetermined region of the build. Microfocus CT imaging was conducted with parameters optimized for Ti-6Al-4V additive manufacture. After CT imaging, a modified RoboMet. 3D version 2 was employed for serial sectioning and optical microscopy characterization of the same predetermined region. Automated montage capture with sub-micron resolution, bright-field reflection, 12-bit monochrome optical images were performed in an automated fashion. These optical images were post-processed to produce 2D and 3D data sets. This processing included thresholding and segmentation to improve visualization of defect features. The defects observed from optical imaging were compared and correlated with the defects observed from CT imaging over the same predetermined region of the specimen. Quantitative results of area fraction and equivalent pore diameters obtained via each method are presented for this correlation. It is shown that Microfocus CT imaging does not capture all inherent defects within this Ti-6Al-4V AM sample. Best practices for this correlative effort are also presented as well as the future direction of research resultant from this current study.

Keywords: additive manufacture, automated serial sectioning, computed tomography, nondestructive evaluation

Procedia PDF Downloads 139

Authors: Ati Moncef

Abstract:

We describe in this paper the results of a phantom of dynamics renal with MAG3. Our phantom consisted of (tow shaped of kidneys, 1 liver). These phantoms were scanned with static and dynamic protocols and compared with clinical data. in a normal conditions we use our phantoms it's possible to acquire a renal images when we can be compared with clinical scintigraphy. In conclusion, Renal phantom also can use in the quality control of a renal scintigraphy.

Keywords: Renal scintigraphy, MAG3, Nuclear medicine, Gamma Camera.

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Authors: Young Kwon Yang, In Sung Kang, Jung Ha Hwang, Jin Chul Park

Abstract:

The present study is a foundational study for performance improvements on isolation wards to prevent proliferation of secondary infection of infectious diseases such as SARS, H1N1, and MERS inside hospitals. Accordingly, the present study conducted an analysis of the effect of sealing mechanisms and filling of openings on ensuring air tightness performance in isolation wards as well as simulation on air currents in improved isolation wards. The study method is as follows. First, previous studies on aerial infection type and mechanism were reviewed, and the review results were utilized as basic data of analysis on simulation of air current. Second, national and international legislations and regulations in relation to isolation wards as well as case studies on developed nations were investigated in order to identify the problems in isolation wards in Korea and improvement plans. Third, construction and facility plans were compared and analyzed between general and isolation wards focusing on large general hospitals in Korea, thereby conducting comparison and analysis on the performance and effects of air-tightness of general and isolation wards through CFD simulations. The study results showed that isolation wards had better air-tightness performance than that of general wards.

Keywords: AII Room, air-borne infection, CFD, computational fluid dynamics

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Authors: Halimatu S. Abdullahi, Ray E. Sheriff, Fatima Mahieddine

Abstract:

Agriculture is essential to the continuous existence of human life as they directly depend on it for the production of food. The exponential rise in population calls for a rapid increase in food with the application of technology to reduce the laborious work and maximize production. Technology can aid/improve agriculture in several ways through pre-planning and post-harvest by the use of computer vision technology through image processing to determine the soil nutrient composition, right amount, right time, right place application of farm input resources like fertilizers, herbicides, water, weed detection, early detection of pest and diseases etc. This is precision agriculture which is thought to be solution required to achieve our goals. There has been significant improvement in the area of image processing and data processing which has being a major challenge. A database of images is collected through remote sensing, analyzed and a model is developed to determine the right treatment plans for different crop types and different regions. Features of images from vegetations need to be extracted, classified, segmented and finally fed into the model. Different techniques have been applied to the processes from the use of neural network, support vector machine, fuzzy logic approach and recently, the most effective approach generating excellent results using the deep learning approach of convolution neural network for image classifications. Deep Convolution neural network is used to determine soil nutrients required in a plantation for maximum production. The experimental results on the developed model yielded results with an average accuracy of 99.58%.

Keywords: convolution, feature extraction, image analysis, validation, precision agriculture

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Authors: Can Zhang, Qun Li, Yonglin Lei, Zhi Zhu, Dong Guo

Abstract:

Aiming at the problem of factor screen in distributed collaborative navigation of dense UAV swarm, an efficient distributed collaborative navigation factor screen method is proposed. The method considered the balance between computing load and positioning accuracy. The proposed algorithm utilized the factor graph model to implement a distributed collaborative navigation algorithm. The GNSS information of the UAV itself and the ranging information between the UAVs are used as the positioning factors. In this distributed scheme, a local factor graph is established for each UAV. The positioning factors of nodes with good geometric position distribution and small variance are selected to participate in the navigation calculation. To demonstrate and verify the proposed methods, the simulation and experiments in different scenarios are performed in this research. Simulation results show that the proposed scheme achieves a good balance between the computing load and positioning accuracy in the distributed cooperative navigation calculation of UAV swarm. This proposed algorithm has important theoretical and practical value for both industry and academic areas.

Keywords: screen method, cooperative positioning system, UAV swarm, factor graph, cooperative navigation

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Authors: Levent Basayigit, Sinan Demir, Burhan Kara, Yusuf Ucar

Abstract:

Cannabis is mostly used for drug production. In some countries, an excessive amount of illegal cannabis is cultivated and sold. Most of the illegal cannabis cultivation occurs on the lands far from settlements. In farmlands, it is cultivated with other crops. In this method, cannabis is surrounded by tall plants like corn and sunflower. It is also cultivated with tall crops as the mixed culture. The common method of the determination of the illegal cultivation areas is to investigate the information obtained from people. This method is not sufficient for the determination of illegal cultivation in remote areas. For this reason, more effective methods are needed for the determination of illegal cultivation. Remote Sensing is one of the most important technologies to monitor the plant growth on the land. The aim of this study is to monitor cannabis cultivation area using satellite imagery. The main purpose of this study was to develop an applicable method for monitoring the cannabis cultivation. For this purpose, cannabis was grown as single or surrounded by the corn and sunflower in plots. The morphological characteristics of cannabis were recorded two times per month during the vegetation period. The spectral signature library was created with the spectroradiometer. The parcels were monitored with high-resolution satellite imagery. With the processing of satellite imagery, the cultivation areas of cannabis were classified. To separate the Cannabis plots from the other plants, the multiresolution segmentation algorithm was found to be the most successful for classification. WorldView Improved Vegetative Index (WV-VI) classification was the most accurate method for monitoring the plant density. As a result, an object-based classification method and vegetation indices were sufficient for monitoring the cannabis cultivation in multi-temporal Earthwiev images.

Keywords: Cannabis, drug, remote sensing, object-based classification

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Authors: Abdelkader Basli, Jean-Claude Delaunay, Eric Pedrot, Jean-Michel Mérillon, Jean-Pierre Monti, Khodir Madani, Mohamed Chibane, Tristan Richard

Abstract:

The antioxidant and antimicrobial activities of Origanum glandulosum collected in Algeria have been studied. Extract was prepared from aerial part of endemic Algerian oregano. The produced extract has been characterized in terms of total phenols (using Folin method), total flavonoid, antioxidant activities (using the DPPH radical scavenging method and ORAC assay) and microbial activity against four bacteria: Streptococcus aureus, Bacillus subtilis, Escherichia coli, Klebsiella pneumoniae one yeast: Candida albicans and one fungi: Aspergillus niger. The results pointed the antioxidant activities of the extract of O. glandulosum and antimicrobial activities against all bacteria and C. Candida, but no effect on A. niger. High performance liquid chromatography combined with mass spectrometry (LC-MS) and nuclear magnetic resonance (LC-NMR) were used to separate and identify the major compounds present in the oregano extract. Rosmarinic acid, globoidnan A and B, lithospermic acid B and three flavonoids were identified.

Keywords: origanum glandulosum, antioxidant, microbial activity, polyphenol, LC-MS, LC-NMR

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Authors: Ahmed Nouasri, Tahar Dob, Toumi Mohamed, Dahmane Dahmane, Soumioa Krimat, Lynda Lamari, Chabane Chelghom

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The aim of this study is to investigate the chemical composition for the first time, and antimicrobial activities of essential oil (EO) of Thymus lanceolatus Desf., an endemic thyme from Tiaret province of Algeria. The chemical composition of hydrodistilled essential oil from flowering aerial parts has been analyzed by GC and GC/MS techniques, the antimicrobial activity was realised by agar disc diffusion method and MIC was determined in solid medium by direct contact. Essential oil of T. lanceolataus has been yielded of 2.336 (w/w) based on dry weight, the analyses cited above, led to the identification of 29 components, which accounted for 97.34% of the total oil. Oxygenated monoterpenes was the main fraction (88.31%) dominated by thymol (80.2%) as major component of this oil, followed by carvacrol (6.25%). The oil was found effective against all tested strains especially fungus, except Pseudomonas aeruginosa were low activity observed, in addition Gram (+) bacteria found to be more sensitive to the EO than Gram (-) bacteria. This activity was ranging from12±2.65mm to 60.00±0.00mm Ø, with the lowest MIC value of under 0.06mg/ml to 12.53mg/ml. This results provided the evidence that the studied plant might indeed be potential sources of natural antimicrobial agents

Keywords: Thymus lanceolatus Desf., essential oil, chemical composition, antimicrobial activities

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Authors: D. P. Rollinson, B. J. Dilley, P. G. Ryan

Abstract:

The white-chinned petrel (Procellaria aequinoctialis) is the seabird species most commonly killed by Southern Hemisphere longline fisheries. Despite the importance of diving ability for mitigating longline bycatch, little is known of this species’ diving behaviour. We obtained data from temperature–depth recorders from nine white-chinned petrels breeding on Marion Island, southwestern Indian Ocean, during the late incubation and chickrearing period. Maximum dive depth (16 m) was slightly deeper than the previous estimate (13 m), but varied considerably among individuals (range 2–16 m). Males dived deeper than females, and birds feeding chicks dived deeper than incubating birds, but dive rate did not differ between the sexes. Time of day had no significant effect on dive depth or rate. Our findings will help to improve the design and performance of mitigation measures aimed at reducing seabird bycatch in longline fisheries, such as the calculation of minimum line sink rates and optimum aerial coverage of bird-scaring lines.

Keywords: dive depth, dive duration, temperature–depth recorders, seabirds, bird-scaring lines

Procedia PDF Downloads 566