Search results for: destination image

Authors: René A. Sørensen, Søren Skovsen, Peter Christiansen, Henrik Karstoft

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Autonomous agricultural machines act in stochastic surroundings and therefore, must be able to perceive the surroundings in real time. This perception can be achieved using image sensors combined with advanced machine learning, in particular Deep Learning. Deep convolutional neural networks excel in labeling and perceiving color images and since the cost of high-quality RGB-cameras is low, the hardware cost of good perception depends heavily on memory and computation power. This paper investigates the possibility of designing lightweight convolutional neural networks for semantic segmentation (pixel wise classification) with reduced hardware requirements, to allow for embedded usage in autonomous agricultural machines. Using compression techniques, a lightweight convolutional neural network is designed to perform real-time semantic segmentation on an embedded platform. The network is trained on two large datasets, ImageNet and Pascal Context, to recognize up to 400 individual classes. The 400 classes are remapped into agricultural superclasses (e.g. human, animal, sky, road, field, shelterbelt and obstacle) and the ability to provide accurate real-time perception of agricultural surroundings is studied. The network is applied to the case of autonomous grass mowing using the NVIDIA Tegra X1 embedded platform. Feeding case-specific images to the network results in a fully segmented map of the superclasses in the image. As the network is still being designed and optimized, only a qualitative analysis of the method is complete at the abstract submission deadline. Proceeding this deadline, the finalized design is quantitatively evaluated on 20 annotated grass mowing images. Lightweight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show competitive performance with regards to accuracy and speed. It is feasible to provide cost-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: autonomous agricultural machines, deep learning, safety, visual perception

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Authors: Qiaosi Li, Frankie Kwan Kit Wong, Tung Fung

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Lightweight unmanned aerial vehicle (UAV) loading with novel sensors offers a low cost approach for data acquisition in complex environment. This study established a framework for applying UAV system in complex environment quick mapping and assessed the performance of UAV-based hyperspectral image and digital surface model (DSM) derived from photogrammetric point clouds for 13 species classification in wetland area Mai Po Inner Deep Bay Ramsar Site, Hong Kong. The study area was part of shallow bay with flat terrain and the major species including reedbed and four mangroves: Kandelia obovata, Aegiceras corniculatum, Acrostichum auerum and Acanthus ilicifolius. Other species involved in various graminaceous plants, tarbor, shrub and invasive species Mikania micrantha. In particular, invasive species climbed up to the mangrove canopy caused damage and morphology change which might increase species distinguishing difficulty. Hyperspectral images were acquired by Headwall Nano sensor with spectral range from 400nm to 1000nm and 0.06m spatial resolution image. A sequence of multi-view RGB images was captured with 0.02m spatial resolution and 75% overlap. Hyperspectral image was corrected for radiative and geometric distortion while high resolution RGB images were matched to generate maximum dense point clouds. Furtherly, a 5 cm grid digital surface model (DSM) was derived from dense point clouds. Multiple feature reduction methods were compared to identify the efficient method and to explore the significant spectral bands in distinguishing different species. Examined methods including stepwise discriminant analysis (DA), support vector machine (SVM) and minimum noise fraction (MNF) transformation. Subsequently, spectral subsets composed of the first 20 most importance bands extracted by SVM, DA and MNF, and multi-source subsets adding extra DSM to 20 spectrum bands were served as input in maximum likelihood classifier (MLC) and SVM classifier to compare the classification result. Classification results showed that feature reduction methods from best to worst are MNF transformation, DA and SVM. MNF transformation accuracy was even higher than all bands input result. Selected bands frequently laid along the green peak, red edge and near infrared. Additionally, DA found that chlorophyll absorption red band and yellow band were also important for species classification. In terms of 3D data, DSM enhanced the discriminant capacity among low plants, arbor and mangrove. Meanwhile, DSM largely reduced misclassification due to the shadow effect and morphological variation of inter-species. In respect to classifier, nonparametric SVM outperformed than MLC for high dimension and multi-source data in this study. SVM classifier tended to produce higher overall accuracy and reduce scattered patches although it costs more time than MLC. The best result was obtained by combining MNF components and DSM in SVM classifier. This study offered a precision species distribution survey solution for inaccessible wetland area with low cost of time and labour. In addition, findings relevant to the positive effect of DSM as well as spectral feature identification indicated that the utility of UAV-borne hyperspectral and photogrammetry deriving 3D data is promising in further research on wetland species such as bio-parameters modelling and biological invasion monitoring.

Keywords: digital surface model (DSM), feature reduction, hyperspectral, photogrammetric point cloud, species mapping, unmanned aerial vehicle (UAV)

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Authors: T. Almutairi, K. Naudi, N. Nairn, X. Ju, B. Eng, J. Whitters, A. Ayoub

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Purpose: At present Cone Beam Computed Tomography (CBCT) imaging does not record dental morphology accurately due to the scattering produced by metallic restorations and the reported magnification. The aim of this pilot study is the development and validation of a new method for the replacement of the distorted dentition of CBCT scans with the dental image captured by the digital intraoral camera. Materials and Method: Six dried skulls with orthodontics brackets on the teeth were used in this study. Three intra-oral markers made of dental stone were constructed which were attached to orthodontics brackets. The skulls were CBCT scanned, and occlusal surface was captured using TRIOS® 3D intraoral scanner. Marker based and surface based registrations were performed to fuse the digital intra-oral scan(IOS) into the CBCT models. This produced a new composite digital model of the skull and dentition. The skulls were scanned again using the commercially accurate Laser Faro® arm to produce the 'gold standard' model for the assessment of the accuracy of the developed method. The accuracy of the method was assessed by measuring the distance between the occlusal surfaces of the new composite model and the 'gold standard' 3D model of the skull and teeth. The procedure was repeated a week apart to measure the reproducibility of the method. Results: The results showed no statistically significant difference between the measurements on the first and second occasions. The absolute mean distance between the new composite model and the laser model ranged between 0.11 mm to 0.20 mm. Conclusion: The dentition of the CBCT can be accurately replaced with the dental image captured by the intra-oral scanner to create a composite model. This method will improve the accuracy of orthognathic surgical prediction planning, with the final goal of the fabrication of a physical occlusal wafer without to guide orthognathic surgery and eliminate the need for dental impression.

Keywords: orthognathic surgery, superimposition, models, cone beam computed tomography

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Authors: Mahdieh Khanmohammadi, Sune Darkner, Nicoletta Nava, Jens Randel Nyengaard, Jon Sporring

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We study the effect of stress on nervous system and we use two experimental groups of rats: sham rats and rats subjected to acute foot-shock stress. We investigate the synaptic vesicles density as a function of distance to the active zone in serial section transmission electron microscope images in 2 and 3 dimensions. By estimating the density in 2D and 3D we compare two groups of rats.

Keywords: stress, 3-dimensional synaptic vesicle density, image registration, bioinformatics

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Authors: Sirajdin Olagoke Adeshina, Haidi Ibrahim, Akeem Salawu

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large lecture theatres cannot be covered by a single camera but rather by a multicamera setup because of their size, shape, and seating arrangements. Although, classroom capture is achievable through a single camera. Therefore, a design and implementation of a multicamera setup for a large lecture hall were considered. Researchers have shown emphasis on the impact of class attendance taken on the academic performance of students. However, the traditional method of carrying out this exercise is below standard, especially for large lecture theatres, because of the student population, the time required, sophistication, exhaustiveness, and manipulative influence. An automated large classroom attendance system is, therefore, imperative. The common approach in this system is face detection and recognition, where known student faces are captured and stored for recognition purposes. This approach will require constant face database updates due to constant changes in the facial features. Alternatively, face counting can be performed by cropping the localized faces on the video or image into a folder and then count them. This research aims to develop a face localization-based approach to detect student faces in classroom images captured using a multicamera setup. A selected Haar-like feature cascade face detector trained with an asymmetric goal to minimize the False Rejection Rate (FRR) relative to the False Acceptance Rate (FAR) was applied on Raspberry Pi 4B. A relationship between the two factors (FRR and FAR) was established using a constant (λ) as a trade-off between the two factors for automatic adjustment during training. An evaluation of the proposed approach and the conventional AdaBoost on classroom datasets shows an improvement of 8% TPR (output result of low FRR) and 7% minimization of the FRR. The average learning speed of the proposed approach was improved with 1.19s execution time per image compared to 2.38s of the improved AdaBoost. Consequently, the proposed approach achieved 97% TPR with an overhead constraint time of 22.9s compared to 46.7s of the improved Adaboost when evaluated on images obtained from a large lecture hall (DK5) USM.

Keywords: automatic attendance, face detection, haar-like cascade, manual attendance

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Authors: William Albeiro Alvarez, Gloria Patricia Jaramillo, Ivan Reinaldo Sarmiento

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Understanding the behavior of individuals and the different human factors that influence the choice when faced with a complex system such as transportation is one of the most complicated aspects of measuring in the components that constitute the modeling of route choice due to that various behaviors and driving mode directly or indirectly affect the choice. During the last two decades, with the development of information and communications technologies, new data collection techniques have emerged such as GPS, geolocation with mobile phones, apps for choosing the route between origin and destination, individual service transport applications among others, where an interest has been generated to improve discrete choice models when considering the incorporation of these developments as well as psychological factors that affect decision making. This paper implements a discrete choice model that proposes and estimates a hybrid model that integrates route choice models and latent variables based on the observation on the route of a sample of public taxi drivers from the city of Medellín, Colombia in relation to its behavior, personality, socioeconomic characteristics, and driving mode. The set of choice options includes the routes generated by the individual service transport applications versus the driver's choice. The hybrid model consists of measurement equations that relate latent variables with measurement indicators and utilities with choice indicators along with structural equations that link the observable characteristics of drivers with latent variables and explanatory variables with utilities.

Keywords: behavior choice model, human factors, hybrid model, real time data

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Authors: Dan Kanmegne

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Africa will become the most populated continent by the end of the century, with around 4 billion inhabitants. Food security and climate changes will become continental issues since agricultural practices depend on climate but also contribute to global emissions and land degradation. Agroforestry has been identified as a cost-efficient and reliable strategy to address these two issues. It is defined as the integrated management of trees and crops/animals in the same land unit. Agroforestry provides benefits in terms of goods (fruits, medicine, wood, etc.) and services (windbreaks, fertility, etc.), and is acknowledged to have a great potential for carbon sequestration; therefore it can be integrated into reduction mechanisms of carbon emissions. Particularly in sub-Saharan Africa, the constraint stands in the lack of information about both areas under agroforestry and the characterization (composition, structure, and management) of each agroforestry system at the country level. This study describes and quantifies “what is where?”, earliest to the quantification of carbon stock in different systems. Remote sensing (RS) is the most efficient approach to map such a dynamic technology as agroforestry since it gives relatively adequate and consistent information over a large area at nearly no cost. RS data fulfill the good practice guidelines of the Intergovernmental Panel On Climate Change (IPCC) that is to be used in carbon estimation. Satellite data are getting more and more accessible, and the archives are growing exponentially. To retrieve useful information to support decision-making out of this large amount of data, satellite data needs to be organized so to ensure fast processing, quick accessibility, and ease of use. A new solution is a data cube, which can be understood as a multi-dimensional stack (space, time, data type) of spatially aligned pixels and used for efficient access and analysis. A data cube for Burkina Faso has been set up from the cooperation project between the international service provider WASCAL and Germany, which provides an accessible exploitation architecture of multi-temporal satellite data. The aim of this study is to map and characterize agroforestry systems using the Burkina Faso earth observation data cube. The approach in its initial stage is based on an unsupervised image classification of a normalized difference vegetation index (NDVI) time series from 2010 to 2018, to stratify the country based on the vegetation. Fifteen strata were identified, and four samples per location were randomly assigned to define the sampling units. For safety reasons, the northern part will not be part of the fieldwork. A total of 52 locations will be visited by the end of the dry season in February-March 2020. The field campaigns will consist of identifying and describing different agroforestry systems and qualitative interviews. A multi-temporal supervised image classification will be done with a random forest algorithm, and the field data will be used for both training the algorithm and accuracy assessment. The expected outputs are (i) map(s) of agroforestry dynamics, (ii) characteristics of different systems (main species, management, area, etc.); (iii) assessment report of Burkina Faso data cube.

Keywords: agroforestry systems, Burkina Faso, earth observation data cube, multi-temporal image classification

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Authors: V. Markogianni, D. Kalivas, G. Petropoulos, E. Dimitriou

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Lake water quality monitoring in combination with the use of earth observation products constitutes a major component in many water quality monitoring programs. Landsat 8 images of Trichonis Lake (Greece) acquired on 30/10/2013 and 30/08/2014 were used in order to explore the possibility of Landsat 8 to estimate water quality parameters and particularly CDOM absorption at specific wavelengths, chlorophyll-a and nutrient concentrations in this oligotrophic freshwater body, characterized by inexistent quantitative, temporal and spatial variability. Water samples have been collected at 22 different stations, on late August of 2014 and the satellite image of the same date was used to statistically correlate the in-situ measurements with various combinations of Landsat 8 bands in order to develop algorithms that best describe those relationships and calculate accurately the aforementioned water quality components. Optimal models were applied to the image of late October of 2013 and the validation of the results was conducted through their comparison with the respective available in-situ data of 2013. Initial results indicated the limited ability of the Landsat 8 sensor to accurately estimate water quality components in an oligotrophic waterbody. As resulted by the validation process, ammonium concentrations were proved to be the most accurately estimated component (R = 0.7), followed by chl-a concentration (R = 0.5) and the CDOM absorption at 420 nm (R = 0.3). In-situ nitrate, nitrite, phosphate and total nitrogen concentrations of 2014 were measured as lower than the detection limit of the instrument used, hence no statistical elaboration was conducted. On the other hand, multiple linear regression among reflectance measures and total phosphorus concentrations resulted in low and statistical insignificant correlations. Our results were concurrent with other studies in international literature, indicating that estimations for eutrophic and mesotrophic lakes are more accurate than oligotrophic, owing to the lack of suspended particles that are detectable by satellite sensors. Nevertheless, although those predictive models, developed and applied to Trichonis oligotrophic lake are less accurate, may still be useful indicators of its water quality deterioration.

Keywords: landsat 8, oligotrophic lake, remote sensing, water quality

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Authors: Armando Luis Vieira, Carlos Costa, Arthur Araújo

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The tourism system is viewable as a network of relationships amongst business partners where the success of each actor will ultimately be determined by the success of the whole network. Especially since the publication of Gümmesson’s (1996) ‘theory of imaginary organisations’, which suggests that organisational effectiveness largely depends on managing relationships and sharing resources and activities, relationship quality (RQ) has been increasingly recognised as a main source of value creation and competitive advantage. However, there is still ambiguity around this topic, and managers and researchers have been recurrently reporting the need to better understand and capitalise on the quality of interactions with business partners. This research aims at testing an RQ model from a relational, imaginary organisation’s approach. Two mail surveys provide the perceptions of 725 hotel representatives about their business relationships with tour operators, and 1,224 corporate client representatives about their business relationships with hotels (21.9 % and 38.8 % response rate, respectively). The analysis contributes to enhance our understanding on the linkages between RQ and its determinants, and identifies the role of their dimensions. Structural equation modelling results highlight trust as the dominant dimension, the crucial role of commitment and satisfaction, and suggest customer orientation as complementary building block. Findings also emphasise problem solving behaviour and selling orientation as the most relevant dimensions of customer orientation. The comparison of the two ‘dyads’ deepens the discussion and enriches the suggested theoretical and managerial guidelines concerning the contribution of quality relationships to business performance.

Keywords: corporate clients, destination competitiveness, hotels, relationship quality, structural equations modelling, tour operators

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Authors: Ruvaitha Banu, Amaladhithyan Krishnamoorthy

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IoT networks today solve various consumer problems, from home automation systems to aiding in driving autonomous vehicles with the exploration of multiple devices. For example, in an autonomous vehicle environment, multiple sensors are available on roads to monitor weather and road conditions and interact with each other to aid the vehicle in reaching its destination safely and timely. IoT systems are predominantly dependent on the cloud environment for data storage, and computing needs that result in latency problems. With the advent of Fog networks, some of this storage and computing is pushed to the edge/fog nodes, saving the network bandwidth and reducing the latency proportionally. Managing the data stored in these fog nodes becomes crucial as it might also store sensitive information required for a certain application. Data management in fog nodes is strenuous because Fog networks are dynamic in terms of their availability and hardware capability. It becomes more challenging when the nodes in the network also live a short span, detaching and joining frequently. When an end-user or Fog Node wants to access, read, or write data stored in another Fog Node, then a new protocol becomes necessary to access/manage the data stored in the fog devices as a conventional static way of managing the data doesn’t work in Fog Networks. The proposed solution discusses a protocol that acts by defining sensitivity levels for the data being written and read. Additionally, a distinct data distribution and replication model among the Fog nodes is established to decentralize the access mechanism. In this paper, the proposed model implements stewardship towards the data stored in the Fog node using the application of Reinforcement Learning so that access to the data is determined dynamically based on the requests.

Keywords: IoT, fog networks, data stewardship, dynamic access policy

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Authors: R. O’Reilly Meehan, D. B. Murray

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In many practical situations, bubbles are dispersed in a liquid phase. Understanding these complex bubbly flows is therefore a key issue for applications such as shell and tube heat exchangers, mineral flotation and oxidation in water treatment. Although a large body of work exists for bubbles rising in an unbounded medium, that of bubbles rising in constricted geometries has received less attention. The particular case of a bubble sliding underneath an inclined surface is common to two-phase flow systems. The current study intends to expand this knowledge by performing experiments to quantify the streamwise flow structures associated with a single sliding air bubble under an inclined surface in quiescent water. This is achieved by means of two-dimensional, two-component particle image velocimetry (PIV), performed with a continuous wave laser and high-speed camera. PIV vorticity fields obtained in a plane perpendicular to the sliding surface show that there is significant bulk fluid motion away from the surface. The associated momentum of the bubble means that this wake motion persists for a significant time before viscous dissipation. The magnitude and direction of the flow structures in the streamwise measurement plane are found to depend on the point on its path through which the bubble enters the plane. This entry point, represented by a phase angle, affects the nature and strength of the vortical structures. This study reconstructs the vorticity field in the wake of the bubble, converting the field at different instances in time to slices of a large-scale wake structure. This is, in essence, Taylor’s ”frozen turbulence” hypothesis. Applying this to the vorticity fields provides a pseudo three-dimensional representation from 2-D data, allowing for a more intuitive understanding of the bubble wake. This study provides insights into the complex dynamics of a situation common to many engineering applications, particularly shell and tube heat exchangers in the nucleate boiling regime.

Keywords: bubbly flow, particle image velocimetry, two-phase flow, wake structures

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Authors: Idowu Olugbenga Adewumi, Foluke Iyabo Oluwatoyinbo

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Presently, there are several discussions on the food security with increase in yield of arable crop throughout the world. This article, briefly present research efforts to create digital interfaces to nature, in particular to area of crop production in agriculture with increase in yield with interest on pervasive computing. The approach goes beyond the use of sensor networks for environmental monitoring but also by emphasizing the development of a system architecture that detect intruder (Intrusion Process) which reduce the yield of the farmer at the end of the planting/harvesting period. The objective of the work is to set a model for setting up the hand held or portable device for increasing the quality and quantity of arable crop. This process incorporates the use of infrared motion image sensor with security alarm system which can send a noise signal to intruder on the farm. This model of the portable image sensing device in monitoring or scaring human, rodent, birds and even pests activities will reduce post harvest loss which will increase the yield on farm. The nano intelligence technology was proposed to combat and minimize intrusion process that usually leads to low quality and quantity of produce from farm. Intranet system will be in place with wireless radio (WLAN), router, server, and client computer system or hand held device e.g PDAs or mobile phone. This approach enables the development of hybrid systems which will be effective as a security measure on farm. Since, precision agriculture has developed with the computerization of agricultural production systems and the networking of computerized control systems. In the intelligent plant production system of controlled greenhouses, information on plant responses, measured by sensors, is used to optimize the system. Further work must be carry out on modeling using pervasive computing environment to solve problems of agriculture, as the use of electronics in agriculture will attracts more youth involvement in the industry.

Keywords: pervasive computing, intrusion detection, precision agriculture, security, arable crop

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Authors: Jianrong Wu, Xuan Fu, Akihiro Higashi, Zhiming Tan

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CMOS image sensors with a rolling shutter are used broadly in the digital cameras embedded in mobile devices. The rolling shutter suffers the flicker artifacts from the fluorescent lamp, and it could be observed easily. In this paper, the characteristics of illumination flicker in motion case were analyzed, and two efficient detection methods based on matching fragment selection were proposed. According to the experimental results, our methods could achieve as high as 100% accuracy in static scene, and at least 97% in motion scene.

Keywords: illumination flicker, embedded camera, rolling shutter, detection

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Authors: Evan Lowhorn, Rocio Alba-Flores

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The purpose of this work is to develop a method for classifying hand signals and using the output in a drone control algorithm. To achieve this, methods based on Convolutional Neural Networks (CNN) were applied. CNN's are a subset of deep learning, which allows grid-like inputs to be processed and passed through a neural network to be trained for classification. This type of neural network allows for classification via imaging, which is less intrusive than previous methods using biosensors, such as EMG sensors. Classification CNN's operate purely from the pixel values in an image; therefore they can be used without additional exteroceptive sensors. A development bench was constructed using a desktop computer connected to a high-definition webcam mounted on a scissor arm. This allowed the camera to be pointed downwards at the desk to provide a constant solid background for the dataset and a clear detection area for the user. A MATLAB script was created to automate dataset image capture at the development bench and save the images to the desktop. This allowed the user to create their own dataset of 12,000 images within three hours. These images were evenly distributed among seven classes. The defined classes include forward, backward, left, right, idle, and land. The drone has a popular flip function which was also included as an additional class. To simplify control, the corresponding hand signals chosen were the numerical hand signs for one through five for movements, a fist for land, and the universal “ok” sign for the flip command. Transfer learning with PyTorch (Python) was performed using a pre-trained 18-layer residual learning network (ResNet-18) to retrain the network for custom classification. An algorithm was created to interpret the classification and send encoded messages to a Ryze Tello drone over its 2.4 GHz Wi-Fi connection. The drone’s movements were performed in half-meter distance increments at a constant speed. When combined with the drone control algorithm, the classification performed as desired with negligible latency when compared to the delay in the drone’s movement commands.

Keywords: classification, computer vision, convolutional neural networks, drone control

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Authors: Teo Yen Nee, Tham Siew Yean, Andrew Kam Jia Yi

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There are growing concerns about the effect of increasing outward foreign direct investment (OFDI) from Malaysia. These concerns emerged when OFDI surpassed inward FDI for the first time in 2007 and in the subsequent years as well. From a theoretical point of view, the effect of OFDI on exports remains inconclusive depending on the types and/or motivations of investment. Therefore, the objective of this paper is to investigate the effect of Malaysia’s OFDI on manufacturing exports, using a reduced form exports model. The manufacturing data used in this study covered 24 manufacturing industries for the period 2003-2010. The manufacturing sector is the fourth largest sector invested by Malaysia’s OFDI abroad. However, this sector is chosen for this study because total manufacturing trade contributed significantly to Malaysia’s economy growth as reflected by its significant share in the country’s gross domestic product (138.7%) in 2013. Furthermore, Malaysia’s exports are dominated by manufacturing goods. Consequently, the drastic increase in OFDI added concerns about its impact on the country’s exports. Since OFDI activities are still relatively new in Malaysia, this study is exploratory in nature due to a lack of firm level data. Using industry level panel data, the value added of this paper is to meet the research gap by examining the effect of Malaysia’s outward FDI on manufacturing exports. Overall, the findings show that lagged inward FDI, technology development, and industry size are found to positive and significantly influence manufacturing exports as compared to other factors. The insignificant impact of OFDI on manufacturing exports suggests market seeking investment is the main form of OFDI from Malaysia and the destination markets are not served by exports before so that there are no new exports created or displacement of exports. While the results show that there is no need to worry about OFDI’s negative impact on exports, policies should be undertaken to encourage OFDI from Malaysia to create new exports for the country.

Keywords: OFDI, manufacturing industries, exports, Malaysia

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Authors: V. V. Fomin, A. P. Mikhailovich, E. M. Agapitov, V. E. Rogachev, E. A. Kostousova, E. S. Perekhodova

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Ground-level landscape photos can be used as a source of objective data on woody vegetation and vegetation dynamics. We proposed a method for processing, analyzing, and presenting ground photographs, which has the following advantages: 1) researcher has to form holistic representation of the study area in form of a set of interlapping ground-level landscape photographs; 2) it is necessary to define or obtain characteristics of the landscape, objects, and phenomena present on the photographs; 3) it is necessary to create new or supplement existing textual descriptions and annotations for the ground-level landscape photographs; 4) single or multiple ground-level landscape photographs can be used to develop specialized geoinformation layers, schematic maps or thematic maps; 5) it is necessary to determine quantitative data that describes both images as a whole, and displayed objects and phenomena, using algorithms for automated image analysis. It is suggested to match each photo with a polygonal geoinformation layer, which is a sector consisting of areas corresponding with parts of the landscape visible in the photos. Calculation of visibility areas is performed in a geoinformation system within a sector using a digital model of a study area relief and visibility analysis functions. Superposition of the visibility sectors corresponding with various camera viewpoints allows matching landscape photos with each other to create a complete and wholesome representation of the space in question. It is suggested to user-defined data or phenomenons on the images with the following superposition over the visibility sector in the form of map symbols. The technology of geoinformation layers’ spatial superposition over the visibility sector creates opportunities for image geotagging using quantitative data obtained from raster or vector layers within the sector with the ability to generate annotations in natural language. The proposed method has proven itself well for relatively open and clearly visible areas with well-defined relief, for example, in mountainous areas in the treeline ecotone. When the polygonal layers of visibility sectors for a large number of different points of photography are topologically superimposed, a layer of visibility of sections of the entire study area is formed, which is displayed in the photographs. Also, as a result of this overlapping of sectors, areas that did not appear in the photo will be assessed as gaps. According to the results of this procedure, it becomes possible to obtain information about the photos that display a specific area and from which points of photography it is visible. This information may be obtained either as a query on the map or as a query for the attribute table of the layer. The method was tested using repeated photos taken from forty camera viewpoints located on Ray-Iz mountain massif (Polar Urals, Russia) from 1960 until 2023. It has been successfully used in combination with other ground-based and remote sensing methods of studying the climate-driven dynamics of woody vegetation in the Polar Urals. Acknowledgment: This research was collaboratively funded by the Russian Ministry for Science and Education project No. FEUG-2023-0002 (image representation) and Russian Science Foundation project No. 24-24-00235 (automated textual description).

Keywords: woody, vegetation, repeated, photographs

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Authors: Himanshu Sharma, Karthik Kumar, Harish Kumar

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The major problem in crop cultivation is the occurrence of multiple crop diseases. During the growth stage, timely identification of crop diseases is paramount to ensure the high yield of crops, lower production costs, and minimize pesticide usage. In most cases, crop diseases produce observable characteristics and symptoms. The Surveyors usually diagnose crop diseases when they walk through the fields. However, surveyor inspections tend to be biased and error-prone due to the nature of the monotonous task and the subjectivity of individuals. In addition, visual inspection of each leaf or plant is costly, time-consuming, and labour-intensive. Furthermore, the plant pathologists and experts who can often identify the disease within the plant according to their symptoms in early stages are not readily available in remote regions. Therefore, this study specifically addressed early detection of leaf scald, red rot, and eyespot types of diseases within sugarcane plants. The study proposes a computer vision-based approach using a convolutional neural network (CNN) for automatic identification of crop diseases. To facilitate this, firstly, images of sugarcane diseases were taken from google without modifying the scene, background, or controlling the illumination to build the training dataset. Then, the testing dataset was developed based on the real-time collected images from the sugarcane field from India. Then, the image dataset is pre-processed for feature extraction and selection. Finally, the CNN-based Visual Geometry Group (VGG) model was deployed on the training and testing dataset to classify the images into diseased and healthy sugarcane plants and measure the model's performance using various parameters, i.e., accuracy, sensitivity, specificity, and F1-score. The promising result of the proposed model lays the groundwork for the automatic early detection of sugarcane disease. The proposed research directly sustains an increase in crop yield.

Keywords: automatic classification, computer vision, convolutional neural network, image processing, sugarcane disease, visual geometry group

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Authors: Preetham Shankapal, Jill King, Kori Murray, Corby Martin, Paula Giselman, Jason Hicks, Owen Carmicheal

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Activation of reward and satiety networks in the brain while processing palatable food cues, as well as functional connectivity during rest has been studied using functional Magnetic Resonance Imaging of the brain in various obesity phenotypes. However, functional connectivity within the reward and satiety network during food cue processing is understudied. 14 obese individuals underwent two fMRI scans during viewing of Macronutrient Picture System images. Each scan included two blocks of images of High Sugar/High Fat (HSHF), High Carbohydrate/High Fat (HCHF), Low Sugar/Low Fat (LSLF) and also non-food images. Seed voxels within seven food reward relevant ROIs: Insula, putamen and cingulate, precentral, parahippocampal, medial frontal and superior temporal gyri were isolated based on a prior meta-analysis. Beta series correlation for task-related functional connectivity between these seed voxels and the rest of the brain was computed. Voxel-level differences in functional connectivity were calculated between: first and the second scan; individuals who saw novel (N=7) vs. Repeated (N=7) images in the second scan; and between the HC/HF, HSHF blocks vs LSLF and non-food blocks. Computations and analysis showed that during food image viewing, reward network ROIs showed significant functional connectivity with each other and with other regions responsible for attentional and motor control, including inferior parietal lobe and precentral gyrus. These functional connectivity values were heightened among individuals who viewed novel HS/HF images in the second scan. In the second scan session, functional connectivity was reduced within the reward network but increased within attention, memory and recognition regions, suggesting habituation to reward properties and increased recollection of previously viewed images. In conclusion it can be inferred that Functional Connectivity within reward network and between reward and other brain regions, varies by important experimental conditions during food photography viewing, including habituation to shown foods.

Keywords: fMRI, functional connectivity, task-based, beta series correlation

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Authors: Toumi Farah, Charallah Salima

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In the Algerian Sahara, goat farming is predominant, and it’s associated with other types of breeding, particularly camel and sheep; it also constitutes a significant proportion of breeding exclusively goat. This Saharan goat is a small ruminant with a black dress with white’s spots, hanging ears, and a coat more or less long. It is known for its hardiness and resistance to adverse conditions of arid zones and its perfect ecophysiological adaptation to harsh environmental conditions. However, pregnancy alterations, particularly abortion, degrade its productivity and cause economic losses, having both direct and indirect effects on animal production, like the costs of veterinary interventions and the reconstitution of livestock. The purpose of this work is to study the histological aspect of the liver of goats’ aborted living under nomadic herds in the region of Béni-Abbès (30° 7' N, 2° 10 'O). The organs were collected in physiological serum, rinsed, and then fixed with formaldehyde (37°, diluted at 10%). After that, these samples were processed for a topographic study. The morphometric study of the liver was performed by using an image analysis and processing software "Image J"; the various measurements obtained are intended to specify the supposed stage of development according to the body weight. The histological structure of the liver shows that the hepatic parenchyma consists of vascular conjunctive spaces surrounded by Glisson’s capsule. The sinusoids and hepatic portal vein are full of red blood cells, representing sinusoidal congestion and a thrombosed vein. At high magnification, the blood vessels show the presence of vascular thrombosis and haemorrhage in some areas of the hepatic parenchyma. Morphometric analysis shows that the number of liver parenchymal cells and the diameter of liver vessels vary according to the stage of development. The results obtained will provide details of the anatomical and cellular elements that can be used in the diagnosis of early or late abortion and late embryonic death. It would be interesting to find, by immunohistochemistry, some inflammatory markers useful for monitoring the progress of pregnancy and bioindicators of fetomaternal distress.

Keywords: aborting goat, arid zone, liver, histopathology

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Authors: Giovanna Borges Bottino, Luis Felipe Freitas do Nascimento Alves Teixeira

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This article aims to present a case study of the National Civil Aviation Agency (ANAC) in Brazil, ANAC-id. ANAC-id is the artificial intelligence algorithm developed for image analysis that recognizes standard images of unobstructed and uprighted face without sunglasses, allowing to identify potential inconsistencies. It combines YOLO architecture and 3 libraries in python - face recognition, face comparison, and deep face, providing robust analysis with high level of accuracy.

Keywords: artificial intelligence, deepface, face compare, face recognition, YOLO, computer vision

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Authors: Elena C. Tamaş, Grațiela M. Țârlea, Gianni Flamaropol, Dragoș Hera

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This article is referring to a comparative study regarding the electrical energy consumption for lighting on diverse types of big sizes commercial buildings built in Romania after 2007, having 3, 4, 5 versus 8, 9, 10 operational years. Some buildings have installed building management systems (BMS) to monitor also the lighting performances starting with the opening days till the present days but some have chosen only local meters to implement. Firstly, for each analyzed building, the total required energy power and the energy power consumption for lighting were calculated depending on the lamps number, the unit power and the average daily running hours. All objects and installations were chosen depending on the destination/location of the lighting (exterior parking or access, interior or covering parking, building interior and building perimeter). Secondly, to all lighting objects and installations, mechanical counters were installed, and to the ones linked to BMS there were installed the digital meters as well for a better monitoring. Some efficient solutions are proposed to improve the power consumption, for example the 1/3 lighting functioning for the covered and exterior parking lighting to those buildings if can be done. This type of lighting share can be performed on each level, especially on the night shifts. Another example is to use the dimmers to reduce the light level, depending on the executed work in the respective area, and a 30% power energy saving can be achieved. Using the right BMS to monitor, the energy consumption depending on the average operational daily hours and changing the non-performant unit lights with the ones having LED technology or economical ones might increase significantly the energy performances and reduce the energy consumption of the buildings.

Keywords: commercial buildings, energy performances, lightning consumption, maintenance

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Authors: Ahmad Baghdadi, Mohammed Kishk

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Construction projects are unique and involve different level of complexity. Airports projects, among other construction projects, are considered to be very complex as they face a number of challenges which make them inevitably exposed to risks. However, in Saudi Arabia, the sector of aviation is considered an important sector owing to the fact that it is the first destination for Muslims on an annual basis. As a result the Saudi government has allocated a huge amount of their general budget to this sector through the General Authority of Civil Aviation (GACA). However, it has been found that the projects are still delivered with a significant number of time and cost overruns. These consequences are typically generated from the risks involved in the projects. Thus, there is a need to identify the number of risks thought to cause such overruns in project times and costs, as well as to assess their significances in terms of their likelihoods of occurrence and their impacts. Accordingly, this paper aims to identify risks associated with aviation construction projects in Saudi Arabia, as well as to assess their likelihoods of occurrence and impacts on such projects. In total, forty four risks have been identified through a critical literature review of common risks in similar projects, as well as thirteen semi-structured interviews with expert project managers involved in GACA’s projects. However, the assessment of the identified risks in term of their likelihoods of occurrence and impacts was obtained through the analysis of forty five questionnaires. Respondents of questionnaires include clients, contractors and consultants. The results show the risks of design changes by the client, labour issue, and setting a tight schedule by the client have the highest likelihoods of occurrence in GACA projects, while the risks of earthquakes, design constructability, and corruption have the greatest impacts.

Keywords: aviation construction projects, GACA, risks, risk assessment, Saudi Arabia

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Authors: Awangku Hassanal Bahar Pengiran Bagul, Fauziahton Ag. Samad

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The focus of this study is to outline the development of Community-Based Ecotourism (CBET) in order to achieve sustainability. The CBET in Desa Cinta Kobuni is a result of a collaboration between Kampung Kobuni, Kota Kinabalu City Hall or DBKK (Dewan Bandaraya Kota Kinabalu), and Universiti Malaysia Sabah (UMS). It is located in Inanam, a sub-district of Kota Kinabalu city. The current ecotourism activities are still in the growth stage and mainly focused on cultural tourism products and activities that showcase their traditional food, clothing, language, history, values, beliefs, dance, arts, and crafts. The study’s methodological approach is qualitative with narrative inquiry, also known as storytelling. This enables the study to access valuable insight with rich data into the complexity of developing community-based ecotourism. The results show that there are three major impacts on the Desa Cinta Kobuni, which are, 1) the increment of secondary income, 2) the advancement of women’s empowerment, and 3) the enhanced sustainability initiatives of the villagers. The experience in developing their first CBET has resulted in the Kota Kinabalu City Hall producing the Framework for Sustainable Community Based Ecotourism that integrates Sustainable Development Goals and the Global Code of Ethics for Tourism (GCET) for future CBET development in other parts of the city. The paper concludes that there is a significant positive transformation of the village and the villagers while reaffirming that Community-Based ecotourism (CBET) is a sustainable form of tourism that improves the quality of life of hosts at the tourist destination.

Keywords: community, ecotourism, cultural tourism, sustainability, sustainable development

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Authors: Linjie Zhang, Baifen Ren, Xi Zhang, Genwang Liu

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Ship detection is of great significance for both military and civilian applications. Synthetic aperture radar (SAR) with all-day, all-weather, ultra-long-range characteristics, has been used widely. In view of the low time resolution of low orbit SAR and the needs for high time resolution SAR data, GEO (Geosynchronous orbit) SAR is getting more and more attention. Since GEO SAR has short revisiting period and large coverage area, it is expected to be well utilized in marine ship targets monitoring. However, the height of the orbit increases the time of integration by almost two orders of magnitude. For moving marine vessels, the utility and efficacy of GEO SAR are still not sure. This paper attempts to find the feasibility of GEO SAR by giving a GEO SAR simulator of moving ships. This presented GEO SAR simulator is a kind of geometrical-based radar imaging simulator, which focus on geometrical quality rather than high radiometric. Inputs of this simulator are 3D ship model (.obj format, produced by most 3D design software, such as 3D Max), ship's velocity, and the parameters of satellite orbit and SAR platform. Its outputs are simulated GEO SAR raw signal data and SAR image. This simulating process is accomplished by the following four steps. (1) Reading 3D model, including the ship rotations (pitch, yaw, and roll) and velocity (speed and direction) parameters, extract information of those little primitives (triangles) which is visible from the SAR platform. (2) Computing the radar scattering from the ship with physical optics (PO) method. In this step, the vessel is sliced into many little rectangles primitives along the azimuth. The radiometric calculation of each primitive is carried out separately. Since this simulator only focuses on the complex structure of ships, only single-bounce reflection and double-bounce reflection are considered. (3) Generating the raw data with GEO SAR signal modeling. Since the normal ‘stop and go’ model is not available for GEO SAR, the range model should be reconsidered. (4) At last, generating GEO SAR image with improved Range Doppler method. Numerical simulation of fishing boat and cargo ship will be given. GEO SAR images of different posture, velocity, satellite orbit, and SAR platform will be simulated. By analyzing these simulated results, the effectiveness of GEO SAR for the detection of marine moving vessels is evaluated.

Keywords: GEO SAR, radar, simulation, ship

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Authors: Shady S. Hassan, Brijesh K. Tiwari, Gwilym A. Williams, Amit K. Jaiswal

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EU is known as the destination with the highest rate of the coffee consumption per capita in the world. Spent coffee grounds (SCG) are the main by-product of coffee brewing. SCG is either disposed as a solid waste or employed as compost, although the polysaccharides from such lignocellulosic biomass might be used as feedstock for fermentation processes. However, SCG as a lignocellulose have a complex structure and pretreatment process is required to facilitate an efficient enzymatic hydrolysis of carbohydrates. However, commonly used pretreatment methods, such as chemical, physico-chemical and biological techniques are still insufficient to meet optimal industrial production requirements in a sustainable way. Ultrasound is a promising candidate as a sustainable green pretreatment solution for lignocellulosic biomass utilization in a large scale biorefinery. Thus, ultrasound pretreatment of SCG without adding harsh chemicals investigated as a green technology to enhance enzyme hydrolysis. In the present work, ultrasound pretreatment experiments were conducted on SCG using different ultrasound frequencies (25, 35, 45, 130, and 950 kHz) for 60 min. Regardless of ultrasound power, low ultrasound frequency is more effective than high ultrasound frequency in pretreatment of biomass. Ultrasound pretreatment of SCG (at ultrasound frequency of 25 kHz for 60 min) followed by enzymatic hydrolysis resulted in total reducing sugars of 56.1 ± 2.8 mg/g of biomass. Fourier transform Infrared Spectroscopy (FTIR) was employed to investigate changes in functional groups of biomass after pretreatment, while high-performance liquid chromatography (HPLC) was employed for determination of glucose. Pretreatment of lignocellulose by low frequency ultrasound in water only was found to be an effective green approach for SCG to improve saccharification and glucose yield compared to native biomass. Pretreatment conditions will be optimized, and the enzyme hydrolysate will be used as media component substitute for the production of ethanol.

Keywords: lignocellulose, ultrasound, pretreatment, spent coffee grounds

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Authors: Vandeyar Saloshna, Vandeyar Hirusellvan

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The image of post-apartheid South African Society that is reflected in the social mirror of the world is largely one of hope, faith, and aspiration. But is this reality? Utilizing social constructivism, case study approach and narrative inquiry, this chapter set out to explore the reflection of South African students from the lens of immigrant students. The picture that unfolds is troublesome in its negativity. In this chapter, we establish in detail what this picture is about and what implications it holds for South African Society.

Keywords: immigrant students, social mirror, xenophobia, identity formation, makwerekwere, expectations

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Authors: Mohamed Ahmed Madkour, Haitham Magdy Hamad

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This research aims to study of the surgery science and imaging in ancient Egypt, and how to diagnose the surgical cases, whether due to injuries or disease that requires surgical intervention, Medical diagnosis and how to treat it. The ancient Egyptian physician tried to change over from magic and theological thinking to become a stand-alone experimental science, they were able to distinguish between diseases and they divide them into internal and external diseases even this division exists to date in modern medicine. There is no evidence to recognize the amount of human knowledge in the prehistoric knowledge of medicine and surgery except skeleton. It is not far from the human being in those times familiar with some means of treatment, Surgery in the Stone age was rudimentary, Flint stone was used after trimming in a certain way as a lancet to slit and open the skin. Wooden tree branches were used to make splints to treat bone fractures. Surgery developed further when copper was discovered, it led to the advancement of Egyptian civilization, then modern and advanced tools appeared in the operating theater like a knife or a scalpel. The climate and environmental conditions have preserved medical papyri and human remains that have confirmed their knowledge of surgical methods including sedation. The ancient Egyptians reached a great importance in surgery, evidenced by the scenes that depict the pathological image and the surgical process, but the image alone is not sufficient to prove the pathology, its presence in ancient Egypt and its treatment method. As there are a number of medical papyri, especially Edwin Smith and Ebris, which prove the ancient Egyptian surgeon's knowledge of the pathological condition that It requires a surgical intervention, otherwise its diagnosis and the method of treatment will not be described with such accuracy through these texts. Some surgeries are described in the department of surgery at Ebris papyrus. The level of surgery in ancient Egypt was high, and they performed surgery such as hernias and Aneurysm, however we have not received a lengthy explanation of the various surgeries and the surgeon has usually only said “treated surgically”. It is evident in the Ebris papyrus that they used sharp surgical tools and cautery in operations where bleeding is expected, such as hernias, arterial sacs and tumors.

Keywords: ancient Egypt, archaeology, Egyptian history, ancient asurgical imaging, Egyptian civilization, civilization

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Authors: Olabisi Badmos, Daniel Callo-Concha, Babatunde Agbola, Andreas Rienow, Klaus Greve, Carsten Jurgens

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Slums are important components of city development planning, especially in Africa where slum growth is on par with urban growth. Purposefully, our knowledge on the residential choice of slum dwellers, which contributes to population growth in slums, is limited. This is the case in Lagos, a megacity reportedly dominated by slum dwellers. Thus, this study aims to disclose the factors influencing the residential choices and causes of people to remain in Lagos slums. Data was collected through questionnaire administration and focus group discussions. Descriptive statistics were used to analyze and describe the factors influencing residential location choice; logistic regression was utilized to determine the extent to which the neighborhood and household attributes, influence slum dwellers decisions to remain in the slums. Results showed that movement to Lagos was the main cause of population growth in slums; most of the migrants were from closer geopolitical zones (in Nigeria). Further, the movement patterns observed support two theories of human mobility in slums: slum as a sink, and as a final destination. Also, the factors that brought most of the slum dwellers to the slums (cheap housing, proximity to work etc.) differs from the ones that made them stay (Gender, employment status, housing status etc.). This study concludes that residential choice and intention to stay are the major contributors to population growth in a slum. It is therefore important for Lagos state Government to incorporate these elements of residential choices of slum dwellers in their slum management policies if the city aims to be free of slums by 2030

Keywords: Lagos, population growth, residential decision choices, slum

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Authors: Selifontova Darya Yurievna

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The study of the process of the emergence and evolution of socio-cultural ideas about the "Asian states" in the context of the development of US cinema in 1941-1945 will contribute both to the approbation of a new approach to the classical subject and will allow using the methodological tools of history, political science, philology, sociology for understanding modern military-political, historical, ideological, socio-cultural processes on a concrete example. This is especially important for understanding the process of constructing the image of the Japanese Empire in the USA. Assessments and images of China and Japan in World War II, created in American cinema, had an immediate impact on the media, public sentiment, and opinions. During the war, the US cinema created new myths and actively exploited old ones, combining them with traditional Hollywood cliches - all this served as a basis for creating the image of China and the Japanese Empire on the screen, which were necessary to solve many foreign policy and domestic political tasks related to the construction of two completely different, but at the same time, similar images of Asia (China and the Japanese Empire). In modern studies devoted to the history of wars, the study of the specifics of the information confrontation of the parties is in demand. A special role in this confrontation is played by propaganda through cinema, which uses images, historical symbols, and stable metaphors, the appeal to which can form a certain public reaction. Soviet documentaries of the war years are proof of this. The relevance of the topic is due to the fact that cinema as a means of propaganda was very popular and in demand during the Second World War. This period was the time of creation of real masterpieces in the field of propaganda films, in the documentary space of the cinema of 1941 – 1945. The traditions of depicting the Second World War were laid down. The study of the peculiarities of visualization and mythologization of the Second World War in Soviet cinema is the most important stage for studying the development of the specifics of propaganda methods since the methods and techniques of depicting the war formed in 1941-1945 are also significant at the present stage of the study of society.

Keywords: asian countries, politics, sociology, domestic politics, USA, cinema

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Authors: Derya Gül Ünlü, Oguz Kuş

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The branding of a country means that the country has its own position different from other countries in its region and thus it is perceived more specifically. It is made possible by the branding efforts of a country and the uniqueness of all the national structures, by presenting it in a specific way, by creating the desired image and attracting tourists and foreign investors. Establishing a national brand involves, in a sense, the process of managing the perceptions of the citizens of the other country about the target country, by structuring the image of the country permanently and holistically. By this means, countries are not easily affected by their crisis of international relations. Therefore, within the scope of the research that will be carried out from this point, it is aimed to show how the warplane downing crisis between Turkey and Russia is perceived on social media. The Russian warplane was downed by Turkey on November 24, 2015, on the grounds that Turkey violated the airspace on the Syrian border. Whereupon the relations between the two countries have been tensed, and Russia has called on its citizens not to go to Turkey and citizens in Turkey to return to their countries. Moreover, relations between two countries have been weakened, for example, tourism tours organized in Russia to Turkey and visa-free travel were canceled and all military dialogue was cut off. After the event, various news sites on social media published plenty of news related to topic and the readers made various comments about the event and Turkey. In this context, an investigation into the perception of Turkey's national brand before and after the warplane downing crisis has been conducted. through comments fetched from the reports on the BBC World, and from Al Jazeera English news sites on Facebook accounts, which takes place widely in the social media. In order to realize study, user comments were fetched from jet downing-related news which are published on Facebook fan-page of BBC World Service, and Al Jazeera English. Regarding this, all the news published between 24.10.2015-24.12.2015 and containing Turk and Turkey keyword in its title composed data set of our study. Afterwards, comments written to these news were analyzed via text mining technique. Furthermore, by sentiment analysis, it was intended to reveal reader’s emotions before and after the crisis.

Keywords: Al Jazeera English, BBC World, country branding, social media, text mining

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