Search results for: Drosophila driver image

Authors: Chijindu D. Mgbemere

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Mass killing also known as genocide is the systematic killing of people from national, ethnic, or religious group, or an attempt to do so. The act has been there before 1948, when it was officially recognized for what it is. From then, the world has continued to witness genocide in diverse forms- negating different measures by the United Nations and its agencies to curb it. So far, all the studies and documentations on this subject are biased in favor of radio and the print. This paper therefore extended the interrogation of genocide, drumming its devastating effects, using the film medium; and in doing so devised innovative and pragmatic approach to genocide scholarship. It further centered attention on the factors and impacts of genocide, with a view to determine how effective film can be in such a study. The study is anchored on Bateson’s Framing Theory. Four films- Hotel Rwanda, Half of a Yellow Sun, Attack on Darfur, and sarafina, were analyzed, based on background, factors/causes, impacts, and development of genocide, via Content Analysis. The study discovered that: as other continents strive towards peace, acts of genocide are on the increase in African. Bloodletting stereotypes give Africa negative image in the global society. Difficult political frameworks, the trauma of postcolonial state, aggravated by ethnic and religious intolerance, and limited access to resources are responsible for high cases of genocide in Africa. The media, international communities, and peace agencies often abet other than prevent genocide or mass killings in Africa. High human casualty and displacement, children soldering, looting, hunger, rape, sex-slavery and abuse, mental and psychosomatic stress disorders are some of the impacts of genocide. Genocidaires are either condemned or killed. Grievances can be vented using civil resistance, negotiation, adjudication, arbitration, and mediation. The cinema is an effective means of studying and documenting genocide. Africans must factor the image laundering of their continent into consideration. Punishment of genocidaires without an attempt to de-radicalize them is counterproductive.

Keywords: African film, genocide, framing theory, mass murder

Procedia PDF Downloads 118

Authors: Kherafa Abdennasser

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The application of Information and Communication Technologies (ICT) to the training area led to the creation of this new reality called E-learning. That last one is described like the marriage of multi- media (sound, image and text) and of the internet (diffusion on line, interactivity). Distance learning became an important totality for training and that last pass in particular by the setup of a distance learning platform. In our memory, we will use an open source platform named Dokeos for the management of a distance training of GPS called e-GPS. The learner is followed in all his training. In this system, trainers and learners communicate individually or in group, the administrator setup and make sure of this system maintenance.

Keywords: ICT, E-learning, learning plate-forme, Dokeos, GPS

Procedia PDF Downloads 477

Authors: Luo Jiaoyang, Yu Hongyang

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In this paper, we explore a method that can be used in the working scene of intelligent industrial robots to confirm the identity information of operators to ensure that the robot executes instructions in a sufficiently safe environment. This approach uses three information modalities, namely visible light, depth, and sound. We explored a variety of fusion modes for the three modalities and finally used the joint feature learning method to improve the performance of the model in the case of noise compared with the single-modal case, making the maximum noise in the experiment. It can also maintain an accuracy rate of more than 90%.

Keywords: multimodal, kinect, machine learning, distance image

Procedia PDF Downloads 79

Authors: Rigved Chandrashekhar Nimkhedkar, Rajat Agrawal, Vinay Sharma

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Architects need help with a demand for an expanded skill set to effectively navigate a landscape of evolving opportunities and challenges in the dynamic realm of the architectural profession. This literature and survey-based study investigates the reasons behind architects’ choices of careers, as well as the effects of the evolving architectural scenario. The traditional role of architects in construction projects evolves as they explore diverse career motivations, face financial constraints due to an oversupply of professionals, and experience specialisation and upskilling trends. Architects inherently derive numerous value chains as more and more disciplines have been introduced into the design-construction-operation supply chain. This insight emphasizes the importance of integrating management and entrepreneurial education into architectural education rather than keeping them separate entities. The study reveals the complex nature of the entrepreneurially challenging architectural profession, including cash flow management, market competition, environmental sustainability, and innovation opportunities. Loyal to their professional identity, architects express dissatisfaction while envisioning a future in which they play a more significant role in shaping reputable brands and contributing to education. The study emphasizes the importance of dovetailing management and entrepreneurial education in architecture education in preparing graduates for the industry’s changing nature, emphasising the need for real-world skills. This research contributes insights into the architectural profession’s transformative trajectory, emphasising adaptability, upskilling, and educational enhancements as critical success factors.

Keywords: architects, career path, education, management, specialisation

Procedia PDF Downloads 66

Authors: Ishmael Obaeko Iwara

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The Point of Sale (POS) cash and cashless semi-formal business has emerged as a significant driver of employment in countries like Nigeria and Kenya, similar to other micro and small-scale enterprises. This business model enables individuals to establish cash in/out outlets, offering entrepreneurs and small business owners a lucrative opportunity to generate additional income. However, the benefits extend beyond employment, as the POS model has become an integral part of the payment system in these countries. It facilitates convenient fund transfers, cash deposits, and withdrawals for individuals residing in both urban and rural areas. Given South Africa's high youth unemployment rate and limited banking services in rural households, coupled with a vibrant informal business economy akin to Nigeria and Kenya, the POS model potentially presents a business opportunity for the unemployed and serves as a banking solution for remote communities. Nonetheless, its implementation within South Africa's entrepreneurial landscape remains a subject of contention. Through qualitative research employing a participatory community-led action research approach, this study analyzes feedback, critiques, and potential interventions from various stakeholders, including business actors, grassroots communities, financial institutions, and policymakers. The findings offer crucial insights into the challenges associated with the adoption of the POS model and suggest mitigating factors to facilitate its successful implementation.

Keywords: grassroots entrepreneurs, rural households, POS banking, youth employment

Procedia PDF Downloads 72

Authors: Sanchita Abrol, Kunal Rana, Ankit Dhir, S. K. Gupta

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According to N.R.E.L.’s findings, 700 crore gallons of petrol is used annually to run the air conditioners of passenger vehicles (nearly 6% of total fuel consumption in the USA). Beyond fuel use, the Environmental Protection Agency reported that refrigerant leaks from auto air conditioning units add an additional 5 crore metric tons of carbon emissions to the atmosphere each year. The objective of our project is to deal with this vital issue by carefully modifying the interiors of a car thereby increasing its mileage and the efficiency of its engine. This would consequently result in a decrease in tail emission and generated pollution along with improved car performance. An automatic mechanism, deployed between the front and the rear seats, consisting of transparent thermal insulating sheet/curtain, would roll down as per the requirement of the driver in order to optimize the volume for effective air conditioning, when travelling alone or with a person. The reduction in effective volume will yield favourable results. Even on a mild sunny day, the temperature inside a parked car can quickly spike to life-threatening levels. For a stationary parked car, insulation would be provided beneath its metal body so as to reduce the rate of heat transfer and increase the transmissivity. As a result, the car would not require a large amount of air conditioning for maintaining lower temperature, which would provide us similar benefits. Authors established the feasibility studies, system engineering and primarily theoretical and experimental results confirming the idea and motivation to fabricate and test the actual product.

Keywords: automation, car, cooling insulating curtains, heat optimization, insulation, reduction in tail emission, mileage

Procedia PDF Downloads 277

Authors: Carolina Zambrana, Grover Zurita

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The food loss production that occurs in deficient agricultural production is one of the major problems worldwide. This puts the population's food security and the efficiency of farming investments at risk. It is to be expected that this food security will be achieved with the own and efficient production of each country. It will have an impact on the well-being of its population and, thus, also on food sovereignty. The production losses in quantity and quality occur due to the lack of efficient detection of diseases at an early stage. It is very difficult to solve the agriculture efficiency using traditional methods since it takes a long time to be carried out due to detection imprecision of the main diseases, especially when the production areas are extensive. Therefore, the main objective of this research study is to perform a systematic literature review, of the latest five years, of Precision Agriculture (PA) to be able to understand the state of the art of the set of new technologies, procedures, and optimization processes with Artificial Intelligence (AI). This study will focus on Corns, Potatoes, and Rice diagnostic diseases. The extensive literature review will be performed on Elsevier, Scopus, and IEEE databases. In addition, this research will focus on advanced digital imaging processing and the development of software and hardware for PA. The convolution neural network will be handling special attention due to its outstanding diagnostic results. Moreover, the studied data will be incorporated with artificial intelligence algorithms for the automatic diagnosis of crop quality. Finally, precision agriculture with technology applied to the agricultural sector allows the land to be exploited efficiently. This system requires sensors, drones, data acquisition cards, and global positioning systems. This research seeks to merge different areas of science, control engineering, electronics, digital image processing, and artificial intelligence for the development, in the near future, of a low-cost image measurement system that allows the optimization of crops with AI.

Keywords: precision agriculture, convolutional neural network, deep learning, artificial intelligence

Procedia PDF Downloads 79

Authors: Rudolf Hrach, Stanislav Novak, Vera Hrachova

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Composite and nanocomposite films represent the class of promising materials and are often objects of the study due to their mechanical, electrical and other properties. The most interesting ones are probably the composite metal/dielectric structures consisting of a metal component embedded in an oxide or polymer matrix. Behaviour of composite films varies with the amount of the metal component inside what is called filling factor. The structures contain individual metal particles or nanoparticles completely insulated by the dielectric matrix for small filling factors and the films have more or less dielectric properties. The conductivity of the films increases with increasing filling factor and finally a transition into metallic state occurs. The behaviour of composite films near a percolation threshold, where the change of charge transport mechanism from a thermally-activated tunnelling between individual metal objects to an ohmic conductivity is observed, is especially important. Physical properties of composite films are given not only by the concentration of metal component but also by the spatial and size distributions of metal objects which are influenced by a technology used. In our contribution, a study of composite structures with the help of methods of computational physics was performed. The study consists of two parts: -Generation of simulated composite and nanocomposite films. The techniques based on hard-sphere or soft-sphere models as well as on atomic modelling are used here. Characterizations of prepared composite structures by image analysis of their sections or projections follow then. However, the analysis of various morphological methods must be performed as the standard algorithms based on the theory of mathematical morphology lose their sensitivity when applied to composite films. -The charge transport in the composites was studied by the kinetic Monte Carlo method as there is a close connection between structural and electric properties of composite and nanocomposite films. It was found that near the percolation threshold the paths of tunnel current forms so-called fuzzy clusters. The main aim of the present study was to establish the correlation between morphological properties of composites/nanocomposites and structures of conducting paths in them in the dependence on the technology of composite films.

Keywords: composite films, computer modelling, image analysis, nanocomposite films

Procedia PDF Downloads 393

Authors: Sandeep J. Kumar

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Urbanisation is considered as the primary driver of land use and land cover change that has direct link to population and economic growth. In India, as well as in other developing countries, cities are urbanizing at an alarming rate. This unprecedented and uncontrolled urbanisation can result in urban sprawl. Due to a number of factors, urban sprawl is recognised to be a result of poor planning, inadequate policies, and poor governance. Urban sprawl may be seen as posing a threat to the development of sustainable cities. Hence, it is very essential to manage this. Planning for predicted future growth is critical to avoid the negative effects of urban growth at the local and regional levels. Thiruvananthapuram being the capital city of Kerala is a city of economic success, challenges, and opportunities. Urbanization trends in the city have paved way for Urban Sprawl. This thesis aims to formulate a framework to combat the emerging urban sprawl in the city of Thiruvananthapuram. For that, the first step was to quantify trends of urban growth in Thiruvananthapuram city using Geographical Information System(GIS) and remote sensing techniques. The technique and results obtained in the study are extremely valuable in analysing the land use changes. Secondly, these change in the trends were analysed through some of the critical factors that helped the study to understand the underlying issues of the existing city structure that has resulted in urban sprawl. Anticipating development trends can modify the current order. This can be productively resolved using regional and municipal planning and management strategies. Hence efficient strategies to curb the sprawl in Thiruvananthapuram city have been formulated in this study that can be considered as recommendations for future planning.

Keywords: urbanisation, urban sprawl, geographical information system(GIS), thiruvananthapuram

Procedia PDF Downloads 107

Authors: Roien Qiam, Shoshi Mizokami

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Parking demand in the Central Business District (CBD) has enlarged with the increase of the number of private vehicles due to rapid economic growth, lack of an efficient public transport and traffic management system. This has resulted in low mobility, poor accessibility, serious congestion, high rates of traffic accident fatalities and injuries and air pollution, mainly because people have to drive slowly around to find a vacant spot. With parking pricing and enforcement policy, considerable advancement could be found, and on-street parking spaces could be managed efficiently and effectively. To evaluate parking demand and making parking policy, it is required to understand the current parking condition and driver’s behavior, understand how drivers choose their parking type and location as well as their behavior toward finding a vacant parking spot under parking charges and search times. This study illustrates the result from an observational, revealed and stated preference surveys and experiment. Attained data shows that there is a gap between supply and demand in parking and it has maximized. For the modeling of the parking decision, a choice model was constructed based on discrete choice modeling theory and multinomial logit model estimated by using SP survey data; the model represents the choice of an alternative among different alternatives which are priced on-street, off-street, and illegal parking. Individuals choose a parking type based on their preference concerning parking charges, searching times, access times and waiting times. The parking assignment model was obtained directly from behavioral model and is used in parking simulation. The study concludes with an evaluation of parking policy.

Keywords: CBD, parking demand forecast, parking policy, parking choice model

Procedia PDF Downloads 195

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

Procedia PDF Downloads 396

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)

Procedia PDF Downloads 257

Authors: P. Senthilraja, C. Rukumani Khandhan, M. Palaniappan, S. L. Rama, P. Sai Sushimitha, R. Madhan, J. Vinumathi, N. Vijayarangan

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Nowadays, the logistics achieve a vast improvement in efficient delivery of goods. The technology improvement also helps to improve its development, but still the owners of transport vehicles face problems, i.e., fuel theft in vehicles by the drivers or by an unknown person. There is no proper solution to overcome the problems. This scheme is to determine the amount of fuel that has been stolen and also to determine the position of the vehicle at a particular time using the technologies like GPS, GSM, ultrasonic fuel level sensor and numeric lock system. The ultrasonic sensor uses the ultrasonic waves to calculate the height of the tank up to which the fuel is available. Based on height it is possible to calculate the amount of fuel. The Global Positioning System (GPS) is a satellite-based navigation system. The scientific community uses GPS for its precision timing capability and position information. The GSM provides the periodic information about the fuel level. A numeric lock system has been provided for fuel tank opening lever. A password is provided to access the fuel tank lever and this is authenticated only by the driver and the owner. Once the fuel tank is opened an alert is sent to owner through a SMS including the timing details. Third party monitoring software is a user interface that updates the information automatically into the database which helps to retrieve the data as and when required. Third party monitoring software provides vehicle’s information to the owner and also shows the status of the vehicle. The techniques that are to be proposed will provide an efficient output. This project helps to overcome the theft and hence to put forth fuel economy.

Keywords: fuel theft, third party monitoring software, bioinformatics, biomedicine

Procedia PDF Downloads 391

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

Procedia PDF Downloads 198

Authors: Samil Ozcan

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Airlines companies target different customer segments in consideration of pricing, service quality, flight network, etc. and their brand positioning accords with the marketization strategies developed in the same direction. The object of this study, Turkish Airlines, has many peculiarities regarding its brand positioning as compared to its rivals in the sector. In the first place, it appeals to a global customer group because of its Star Alliance membership and its broad flight network with 315 destination points. The second group in its customer segmentation includes domestic customers. For this group, the company follows a marketing strategy that plays to local culture and accentuates the image of Turkishness as an emotional allurement. The advertisements and publicity projects designed in this regard put little emphasis on the service quality the company offers to its clients; it addresses the emotions of the consumers rather than individual benefits and relies on the historical memory of the nation and shared cultural values. This study examines the publicity work which aims at the second segment customer group focusing on Turkish Airlines’ 85th Anniversary Commercial through a symbolic meaning analysis approach. The commercial presents six stories with undertones of nationalism in its theme. Nationalism is not just the product of collective interests based on reason but a result of patriotism in the sense of loyalty to state and nation and love of ethnic belonging. While nationalism refers to concrete notions such as blood tie, common ancestor, shared history, it is not the actuality of these notions that it draws its real strength but the emotions invested in them. The myths of origin, the idea of common homeland, boundary definitions, and symbolic acculturation have instrumental importance in the development of these commonalities. The commercial offers concrete examples for an analysis of Connor’s definition of nationalism based on emotions. Turning points in the history of the Turkish Republic and the historical mission Turkish Airlines undertook in these moments are narrated in six stories in the commercial with a highly emotional theme. These emotions, in general, depend on collective memory generated by national consciousness. Collective memory is not simply remembering the past. It is constructed through the reconstruction and reinterpretation of the past in the present moment. This study inquires the motivations behind the nationalist emotions generated within the collective memory by engaging with the commercial released for the 85th anniversary of Turkish Airlines as the object of analysis. Symbols and myths can be read as key concepts that reveal the relation between 'identity and memory'. Because myths and symbols do not merely reflect on collective memory, they reconstruct it as well. In this sense, the theme of the commercial defines the image of Turkishness with virtues such as self-sacrifice, helpfulness, humanity, and courage through a process of meaning creation based on symbolic mythologizations like flag and homeland. These virtues go beyond describing the image of Turkishness and become an instrument that defines and gives meaning to Turkish identity.

Keywords: collective memory, emotions, identity, nationalism

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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

Procedia PDF Downloads 278

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: 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

Procedia PDF Downloads 396

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: Matthew Heidman, Susan Dallabrida, Analice Costa

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For infant caregivers, choosing an infant formula for their child can be a difficult task in an already stressful environment of caring for a newborn. There exist several channels that influence purchasing decision of infant formula such as, friends and family and their experiences, health care professionals, social media influencers, as well as standard media marketing. This study sought to identify the key channels by which caregivers obtain information regarding infant formula and help them make their purchasing decision. A digital survey was issued for 90 days in the US (n=121) and 30 days in Mexico (n=88) targeting respondents with children ≤4 years of age. Respondents were asked two key questions regarding the influences on their purchasing decisions: 1) “When choosing a formula brand, what do you do to help you make your decision?”, and 2) “When choosing a formula brand, what is most important to you?”. A list of potential answers was provided for each question and respondents were asked to select all that apply to them. Lastly, respondents were provided a 5-point Likert scale and asked to respond to the statement 3) “I am more likely to buy a particular formula brand if my pediatrician recommends it to me”. For question 1, in the US and Mexico, 76% and 95% of respondents respectively, selected “I ask my pediatrician” which represented the top selection. For question 2, 52% and 45% of respondents respectively, selected “On package “Pediatrician Recommended” claim…” which also represented the top selection. For statement 3, 82% and 89% of respondents respectively, stated that they either “somewhat agree” or “strongly agree” with the statement. For infant caregivers, the pediatrician is a very important channel of influence when it comes to purchasing decision of infant formula. Caregivers clearly see the pediatrician as the arbiter of their child’s nutrition and seek their recommendations for infant formula use. For infant formula manufacturers, it is important that they see the pediatrician as the gatekeeper to this market, and they put resources into medical marketing communication to this health care professional group to ensure success.

Keywords: infant formula, pediatrician, purchasing driver, caregiver

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Authors: Zerihun Yohannes Amare, Belayneh Birku Geremew, Nigatu Melise Kebede, Sisaynew Getahun Amera

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In Ethiopia, agricultural production is predominantly rainfed. The El Niño Southern Oscillation (ENSO) is the driver of climate variability, which affects the agricultural production system in the country. This paper aims to study trends, variability of rainfall, and impacts of El Niño Southern Oscillation (ENSO) on rainfall amount. The study was carried out in Ethiopia's Western Amhara National Regional State, which features a variety of seasons that characterize the nation. Monthly rainfall data were collected from fifteen meteorological stations of Western Amhara. Selected El Niño and La Niña years were also extracted from National Oceanic and Atmospheric Administration (NOAA) from 1986 to 2015. Once the data quality was checked and inspected, the monthly rainfall data of the selected stations were arranged in Microsoft Excel Spreadsheet and analyzed using XLSTAT software. The coefficient of variation and the Mann-Kendall non-parametric statistical test was employed to analyze trends and variability of rainfall and temperature. The long-term recorded annual rainfall data indicated that there was an increasing trend from 1986 to 2015 insignificantly. The rainfall variability was less (Coefficient of Variation, CV = 8.6%); also, the mean monthly rainfall of Western Amhara decreased during El Niño years and increased during La Niña years, especially in the rainy season (JJAS) over 30 years. This finding will be useful to suggest possible adaptation strategies and efficient use of resources during planning and implementation.

Keywords: rainfall, Mann-Kendall test, El Niño, La Niña, Western Amhara, Ethiopia

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Authors: Rachel Luz P. Vivas-rica, Gloria G. Delan, Christine M. Corrales, Alfonso S. Piquero, Irene A. Monte

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ABSTRACT -Selected Integrated Coastal Resource Management Program (ICRMP) sites in Southwestern Cebu were studied employing a stratified proportional sampling method using semi-structured questionnaires. Four hundred sixteen (416) respondents from five barangays with Marine Protected Areas (MPAs) and four barangays without marine sanctuaries were considered in the study. Results showed similarity of socio-economic characteristics in terms of average age, majority were middle aged, and married. Households were male dominated, obtained low education for both MPA and Non-MPA areas. In terms of occupation, majority in both areas engaged in fulltime fishing however part time jobs as carpenter, construction worker, driver or farmer as another income source. Most of the households were nuclear families with average family size of five for both MPA and Non-MPA. Fishing experience ranged from less than 1 year to more than 50 years. Fishing grounds were within the 15 kilometer radius of each considered site. Even if the respondents were totally dependent on fishing as a major source of income, still their income is way below the poverty threshold both in the MPA and Non-MPA areas. This is further explained by the marginality of their fishing implements wherein majority uses gill nets, hook & line, spear and paddle boat in fishing. Their volume of catch from an average of 6 hours fishing expedition ranges from half a kilo to a maximum of 4 kilos. Majority are not members of fishing groups or organizations.

Keywords: integrated coastal resource management program, marine protected areas, socio-economic, poverty threshold

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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: Pedram Behyar, Zahra Bayat

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The internet and web are changing ways of “far reaching scope and potential for transformation of the marketing functions”. The web is developing in a faster rate than any previous new communication medium. The website of destination has become a crucial branding channel, that is why all businesses are changing their way to communicate with their customers to encounter their needs and wants in better ways. Website provides numerous opportunities for businesses to strengthen their relationship with their customers. One of these opportunities is website component that enables internet users to make two-way communication with the businesses.

Keywords: marketing communication, brand image, usability, privacy and security, personalization and customization, responsiveness, customer online web experience

Procedia PDF Downloads 504

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

Procedia PDF Downloads 270