Search results for: Sentinel-2 satellite image

Authors: Yurike, Yonariza, Rudi Febriamansyah, Syafruddin Karimi

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Forest is a natural resource that has an important function as a supporting element of human life. Forest degradation enormous impact on global warming is a reality we have experienced together, that disruption of ecosystems, extreme weather conditions, disruption of water management system watersheds and the threat of natural disasters as floods, landslides and droughts, even disruption food security. Dharmasraya is a district in the province of West Sumatra, which has an area of 92.150 ha of forest, which is largely a former production forest concessions (Forest Management Rights) which is supposed to be a secondary forest. This study answers about the impact of land use in the former concession area Dharmasraya on the environment. The methodology used is the household survey, key informants, and satellite data / GIS. From the results of the study, the former concession area in Dharmasraya experienced a reduction of forest cover over time significantly. Forest concessions should be secondary forests in Dharmasraya, now turned conversion to oil palm plantations. Population pressures and growing economic pressures, resulting in more intensive harvesting. As a result of these forest disturbances caused changes in forest functions. These changes put more emphasis towards economic function by ignoring social functions or ecological function. Society prefers to maximize their benefits today and pay less attention to the protection of natural resources. This causes global warming is increasing and this is not only felt by people around Dharmasraya but also the world. Land clearing by the community through a process in slash and burn. This fire was observed by NOAA satellites and recorded by the Forest Service of West Sumatra. This demonstrates the ability of trees felled trees to absorb carbon dioxide (CO2) to be lost, even with forest fires accounted for carbon dioxide emitted into the air, and this has an impact on global warming. In addition to the change of control of land into oil palm plantations water service has been poor, people began to trouble the water and oil palm plantations are located in the watershed caused the river dried up. Through the findings of this study is expected to contribute ideas to the policy makers to pay more attention to the former concession forest management as the prevention or reduction of global warming.

Keywords: climate change, community, concession forests, environment

Procedia PDF Downloads 319

Authors: Javier Barrachina, Piedad Garrido, Manuel Fogue, Julio A. Sanguesa, Francisco J. Martinez

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It is very common to observe, especially in Computer Science studies that students have difficulties to correctly understand how some mechanisms based on Artificial Intelligence work. In addition, the scope and limitations of most of these mechanisms are usually presented by professors only in a theoretical way, which does not help students to understand them adequately. In this work, we focus on the problems found when teaching Evolutionary Algorithms (EAs), which imitate the principles of natural evolution, as a method to solve parameter optimization problems. Although this kind of algorithms can be very powerful to solve relatively complex problems, students often have difficulties to understand how they work, and how to apply them to solve problems in real cases. In this paper, we present two interactive graphical applications which have been specially designed with the aim of making Evolutionary Algorithms easy to be understood by students. Specifically, we present: (i) TSPS, an application able to solve the ”Traveling Salesman Problem”, and (ii) FotEvol, an application able to reconstruct a given image by using Evolution Strategies. The main objective is that students learn how these techniques can be implemented, and the great possibilities they offer.

Keywords: education, evolutionary algorithms, evolution strategies, interactive learning applications

Procedia PDF Downloads 328

Authors: Alireza Ansariyar, Mansoureh Jeihani

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Light Detection and Ranging (LiDAR) sensors are capable of recording traffic data including the number of passing vehicles and bicyclists, the speed of vehicles and bicyclists, and the number of conflicts among both road users. In order to collect real-time traffic data and investigate the safety of different road users, a LiDAR sensor was installed at Cold Spring Ln – Hillen Rd intersection in Baltimore City. The frequency and severity of collected real-time conflicts were analyzed and the results highlighted that 122 conflicts were recorded over a 10-month time interval from May 2022 to February 2023. By using an innovative image-processing algorithm, a new safety Measure of Effectiveness (MOE) was proposed to recognize the critical zones for bicyclists entering each zone. Considering the trajectory of conflicts, the results of the analysis demonstrated that conflicts in the northern approach (zone N) are more frequent and severe. Additionally, sunny weather is more likely to cause severe vehicle-bike conflicts.

Keywords: LiDAR sensor, post encroachment time threshold (PET), vehicle-bike conflicts, a measure of effectiveness (MOE), weather condition

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Authors: Yumee Kim, Seung-Guk Hyeon, Kukjin Chun

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An adjustable aperture using a liquid crystal is proposed for real-time range detection and obtaining images simultaneously. The adjustable aperture operates as two types of aperture stops which can create two different Depth of Field images. By analyzing these two images, the distance can be extracted from camera to object. Initially, the aperture stop has large size with zero voltage. When the input voltage is applied, the aperture stop transfer to smaller size by orientational transition of liquid crystal molecules in the device. The diameter of aperture stop is 1.94mm and 1.06mm. The proposed device has low driving voltage of 7.0V and fast response time of 6.22m. Compact size aperture of 6×6×1.1 mm3 is assembled in conventional camera which contain 1/3” HD image sensor and focal length of 3.3mm that can be used in autonomous. The measured range was up to 5m. The adjustable aperture has high stability due to no mechanically moving parts. This range sensor can be applied to the various field of 3D depth map application which is the Advanced Driving Assistance System (ADAS), drones and manufacturing machine.

Keywords: adjustable aperture, dual aperture, liquid crystal, ranging and imaging, ADAS, range sensor

Procedia PDF Downloads 374

Authors: Judith Margaret Tymon

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As cities struggle to incorporate informal settlements into the fabric of urban areas, the focus has often been on the provision of housing. This study explores the underlying structure of street networks, with the goal of understanding the morphogenesis of informal settlements through the lens of the access network. As the stages of development progress from infill to consolidation and eventually, to a planned in-situ settlement, the access networks retain the form of the core segments; however, a majority of street patterns are adapted to a grid design to support infrastructure in the final upgraded phase. A case study is presented to examine the street network in the informal settlement of Gobabis Namibia as it progresses from its initial stages to a planned, in-situ, and permanently upgraded development. The Informal Development Stages framework of foundation, infill, and consolidation, as developed by Dr. Jota Samper, is utilized to examine the evolution of street networks. Data is gathered from historical Google Earth satellite images for the time period between 2003 and 2022. The results demonstrate that during the foundation through infill stages, incremental changes follow similar patterns, with pathways extended, lengthened, and densified as housing is created and the settlement grows. In the final stage of consolidation, the resulting street layout is transformed to support the installation of infrastructure; however, some elements of the original street patterns remain. The core pathways remain intact to accommodate the installation of infrastructure and the creation of housing plots, defining the shape of the settlement and providing the basis of the urban form. The adaptations, growth, and consolidation of the street network are critical to the eventual formation of the spatial layout of the settlement. This study will include a comparative analysis of findings with those of recent research performed by Kamalipour, Dovey, and others regarding incremental urbanism within informal settlements. Further comparisons will also include studies of street networks of well-established urban centers that have shown links between the morphogenesis of access networks and the eventual spatial layout of the city. The findings of the study can be used to guide and inform strategies for in-situ upgrading and can contribute to the sustainable development of informal settlements.

Keywords: Gobabis Namibia, incremental urbanism, informal development stages, informal settlements, street networks

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Authors: Isabel Jones

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As adolescence is a formative time, experiences during adolescence often affect the rest of one’s life. Therefore, dating, specifically violence in dating, can have lasting effects on the rest of one’s life. In order to find sources, searches were conducted on PsycINFO, specifically EBSCO, and narrowed down under the criteria that the source contained information about adolescent dating violence rather than adult, and focused on causes, effects, or prevention methods. This literature review examines research regarding the effects and causes of TDV, and then what methods are effective in the prevention of TDV development. This will allow for a clear image of how these prevention methods are effective and why they are important. Effects of TDV extend beyond the physical, including psychological and sexual long-lasting effects. These are caused by a number of concepts, including learned behavior, inhibitory issues/substance abuse, and cultural factors. When both of these are taken into account, preventative measures such as school-based interventions, parental/adult monitoring, and the presence of positive family examples are more clear as to their effectiveness. This literature review may provide further awareness to this public health crisis and give the public a view of how adolescents are affected by TDV on their path from child to adult.

Keywords: adolescence, dating violence, risk factors, predictors, relationship

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Authors: Ramkumar Gunasekaran, John Cater, Vinod Suresh, Haribalan Kumar

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The main objective of this project is to develop a statistical shape model using principal component analysis that could be used for analyzing the shape of the human airway. The ultimate goal of this project is to identify geometric risk factors for diagnosis and management of Obstructive Sleep Apnoea (OSA). Anonymous CBCT scans of 25 individuals were obtained from the Otago Radiology Group. The airways were segmented between the hard-palate and the aryepiglottic fold using snake active contour segmentation. The point data cloud of the segmented images was then fitted with a bi-cubic mesh, and pseudo landmarks were placed to perform PCA on the segmented airway to analyze the shape of the airway and to find the relationship between the shape and OSA risk factors. From the PCA results, the first four modes of variation were found to be significant. Mode 1 was interpreted to be the overall length of the airway, Mode 2 was related to the anterior-posterior width of the retroglossal region, Mode 3 was related to the lateral dimension of the oropharyngeal region and Mode 4 was related to the anterior-posterior width of the oropharyngeal region. All these regions are subjected to the risk factors of OSA.

Keywords: medical imaging, image processing, FEM/BEM, statistical modelling

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Authors: Chung-Ming Lo, Kevin Li-Chun Hsieh

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The central nervous system in the World Health Organization defines grade 2, 3, 4 gliomas according to the aggressiveness. For brain tumors, using image examination would have a lower risk than biopsy. Besides, it is a challenge to extract relevant tissues from biopsy operation. Observing the whole tumor structure and composition can provide a more objective assessment. This study further proposed a computer-aided diagnosis (CAD) system based on a convolutional neural network to quantitatively evaluate a tumor's malignancy from brain magnetic resonance imaging. A total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were collected in the experiment. Transferred parameters from AlexNet were fine-tuned to classify the target brain tumors and achieved an accuracy of 98% and an area under the receiver operating characteristics curve (Az) of 0.99. Without pre-trained features, only 61% of accuracy was obtained. The proposed convolutional neural network can accurately and efficiently classify grade 2, 3, and 4 gliomas. The promising accuracy can provide diagnostic suggestions to radiologists in the clinic.

Keywords: convolutional neural network, computer-aided diagnosis, glioblastoma, magnetic resonance imaging

Procedia PDF Downloads 138

Authors: Payel Datta, Abhishek Das, Abhishek Roychoudhury, Dhiman Chattopadhyay, Tanushyam Chattopadhyay

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Machine learning (ML) and deep learning (DL) is most predominantly used in image/video processing, natural language processing (NLP), audio and speech recognition but not that much used in system performance evaluation. In this paper, authors are going to describe the architecture of an abstraction layer constructed using ML/DL to detect the system failure. This proposed system is used to detect the system failure by evaluating the performance metrics of an IoT service deployment under constrained infrastructure environment. This system has been tested on the manually annotated data set containing different metrics of the system, like number of threads, throughput, average response time, CPU usage, memory usage, network input/output captured in different hardware environments like edge (atom based gateway) and cloud (AWS EC2). The main challenge of developing such system is that the accuracy of classification should be 100% as the error in the system has an impact on the degradation of the service performance and thus consequently affect the reliability and high availability which is mandatory for an IoT system. Proposed ML/DL classifiers work with 100% accuracy for the data set of nearly 4,000 samples captured within the organization.

Keywords: machine learning, system performance, performance metrics, IoT, edge

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Authors: A. W. Chalmers, O. M. van den Broek

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This article examines the relationship between the global financial crisis and corporate social responsibility activities of financial services firms. It challenges the general consensus in existing studies that firms, when faced with economic hardship, tend to jettison CSR commitments. Instead, and building on recent insights into the institutional determinants of CSR, it is argued that firms are constrained in their ability to abandon CSR by the extent to which they are subject to intense public scrutiny by regulators and the news media. This argument is tested in the context of the European sovereign debt crisis drawing on a unique dataset of 170 firms in 15 different countries over a six-year period. Controlling for a battery of alternative explanations and comparing financial service providers to firms operating in other economic sectors, results indicate considerable evidence supporting the main argument. Rather than abandoning CSR during times of economic hardship, financial industry firms ramp up their CSR commitments in order to manage their public image and foster public trust in light of intense public scrutiny.

Keywords: corporate social responsibility (CSR), public scrutiny, global financial crisis, financial services firms

Procedia PDF Downloads 298

Authors: Luo Jin China, Jin Fang

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Countryside has been generally recognized and regarded as a characteristic symbol which presents in human memory for a long time. As a result of the change of times, because of it’s failure to meet the growing needs of the growing life and mental decline, the vast rural area began to decline. But their history feature image which accumulated by the ancient tradition provides people with the origins of existence on the spiritual level, such as "identity" and "belonging", makes people closer to the others in the spiritual and psychological aspects of a common experience about the past, thus the sense of a lack of culture caused by the losing of memory symbols is weakened. So, in the modernization process, how to repair its vitality and transform and planning it in a sustainable way has become a hot topics in architectural and urban planning. This paper aims to break the constraints of disciplines, from the perspective of interdiscipline, using the research methods of systems science to analyze and discuss the theories and methods of rural form factors, which based on the viewpoint of memory in psychology. So, we can find a right way to transform the Rural to give full play to the role of the countryside in the actual use and the shape of history spirits.

Keywords: rural, sustainable renovation, restoration, psychology, memory

Procedia PDF Downloads 566

Authors: Khaled Missaoui, Amina Beldjazia, Rachid Gharzouli, Yamna Djellouli

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Optical remote sensing makes use of visible, near infrared and short-wave infrared sensors to form images of the earth's surface by detecting the solar radiation reflected from targets on the ground. Different materials reflect and absorb differently at different wavelengths. Thus, the targets can be differentiated by their spectral reflectance signatures in the remotely sensed images. In this work, we are interested to study the distribution of vegetation in the massif forest of Boutaleb (North East of Algeria) which suffered between 1998 and 1999 very large fires. In this case, we use remote sensing with Landsat images from two dates (1984 and 2000) to see the results of these fires. Vegetation has a unique spectral signature which enables it to be distinguished readily from other types of land cover in an optical/near-infrared image. Normalized Difference Vegetation Index (NDVI) is calculated with ENVI 4.7 from Band 3 and 4. The results showed a very important floristic diversity in this forest. The comparison of NDVI from the two dates confirms that there is a decrease of the density of vegetation in this area due to repeated fires.

Keywords: remote sensing, boutaleb, diversity, forest

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Authors: T. J. Hasanova, C. N. Imamalieva

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The problem about the movement of the rigid cylinder keeping the vertical position under the influence of running superficial waves in a liquid is considered. The indignation of a falling wave caused by the presence of the cylinder which moves is thus considered. Special decomposition on a falling harmonious wave is used. The problem dares an operational method. For a finding of the original decision, Considering that the image denominator represents a tabular function, Voltaire's integrated equation of the first sort which dares a numerical method is used. Cylinder movement in the continuous environment under the influence of waves is considered in work. Problems are solved by an operational method, thus originals of required functions are looked for by the numerical definition of poles of combinations of transcendental functions and calculation of not own integrals. Using specificity of a task below, Decisions are under construction the numerical solution of the integrated equation of Volter of the first sort that does not create computing problems of the complex roots of transcendental functions connected with search.

Keywords: rigid cylinder, linear interpolation, fluctuations, Voltaire's integrated equation, harmonious wave

Procedia PDF Downloads 314

Authors: Zainab S. Abdullahi, Zakariyya H. Abdullahi, Sahnun Dahiru

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Student attendance in schools is a very important aspect in school management record. In recent years, security systems have become one of the most demanding systems in school. Every institute have its own method of taking attendance, many schools in Nigeria use the old fashion way of taking attendance. That is writing the students name and registration number in a paper and submitting it to the lecturer at the end of the lecture which is time-consuming and insecure, because some students can write for their friends without the lecturer’s knowledge. In this paper, we propose a system that takes attendance using face recognition. There are many automatic methods available for this purpose i.e. biometric attendance, but they all waste time, because the students have to follow a queue to put their thumbs on a scanner which is time-consuming. This attendance is recorded by using a camera attached in front of the class room and capturing the student images, detect the faces in the image and compare the detected faces with database and mark the attendance. The principle component analysis was used to recognize the faces detected with a high accuracy rate. The paper reviews the related work in the field of attendance system, then describe the system architecture, software algorithm and result.

Keywords: attendance system, face detection, face recognition, PCA

Procedia PDF Downloads 353

Authors: Bilel Chalghaf, Mathieu Varin

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Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

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Authors: Guru Prakash, Revanth Dugalam

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A road’s condition often deteriorates due to harsh loading such as overload due to trucks, and severe environmental conditions such as heavy rain, snow load, and cyclic loading. In absence of proper maintenance planning, this results in potholes, wide cracks, bumps, and increased roughness of roads. In this paper, a data-driven model will be developed to detect these damages using vibration and image signals. The key idea of the proposed methodology is that the road anomaly manifests in these signals, which can be detected by training a machine learning algorithm. The use of various machine learning techniques such as the support vector machine and Radom Forest method will be investigated. The proposed model will first be trained and tested with artificially simulated data, and the model architecture will be finalized by comparing the accuracies of various models. Once a model is fixed, the field study will be performed, and data will be collected. The field data will be used to validate the proposed model and to predict the future road’s health condition. The proposed will help to automate the road condition monitoring process, repair cost estimation, and maintenance planning process.

Keywords: SVM, data-driven, road health monitoring, pot-hole

Procedia PDF Downloads 76

Authors: Sabyasachi Mukhopadhyay, Joseph Ogutu, Gundula Bartzke, Hans-Peter Piepho

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Rainfall is a critical component of climate governing vegetation growth and production, forage availability and quality for herbivores. However, reliable rainfall measurements are not always available, making it necessary to predict rainfall values for particular locations through time. Predicting rainfall in space and time can be a complex and challenging task, especially where the rain gauge network is sparse and measurements are not recorded consistently for all rain gauges, leading to many missing values. Here, we develop a flexible Bayesian model for predicting rainfall in space and time and apply it to Narok County, situated in southwestern Kenya, using data collected at 23 rain gauges from 1965 to 2015. Narok County encompasses the Maasai Mara ecosystem, the northern-most section of the Mara-Serengeti ecosystem, famous for its diverse and abundant large mammal populations and spectacular migration of enormous herds of wildebeest, zebra and Thomson's gazelle. The model incorporates geographical and meteorological predictor variables, including elevation, distance to Lake Victoria and minimum temperature. We assess the efficiency of the model by comparing it empirically with the established Gaussian process, Kriging, simple linear and Bayesian linear models. We use the model to predict total monthly rainfall and its standard error for all 5 * 5 km grid cells in Narok County. Using the Monte Carlo integration method, we estimate seasonal and annual rainfall and their standard errors for 29 sub-regions in Narok. Finally, we use the predicted rainfall to predict large herbivore biomass in the Maasai Mara ecosystem on a 5 * 5 km grid for both the wet and dry seasons. We show that herbivore biomass increases with rainfall in both seasons. The model can handle data from a sparse network of observations with many missing values and performs at least as well as or better than four established and widely used models, on the Narok data set. The model produces rainfall predictions consistent with expectation and in good agreement with the blended station and satellite rainfall values. The predictions are precise enough for most practical purposes. The model is very general and applicable to other variables besides rainfall.

Keywords: non-stationary covariance function, gaussian process, ungulate biomass, MCMC, maasai mara ecosystem

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Authors: William Whiteley, Jens Gregor

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In recent developments of direct neural network based positron emission tomography (PET) reconstruction, two prominent architectures have emerged for converting measurement data into images: 1) networks that contain fully-connected layers; and 2) networks that primarily use a convolutional encoder-decoder architecture. In this paper, we present a multi-modal direct PET reconstruction method called MDPET, which is a hybrid approach that combines the advantages of both types of networks. MDPET processes raw data in the form of sinograms and histo-images in concert with attenuation maps to produce high quality multi-slice PET images (e.g., 8x440x440). MDPET is trained on a large whole-body patient data set and evaluated both quantitatively and qualitatively against target images reconstructed with the standard PET reconstruction benchmark of iterative ordered subsets expectation maximization. The results show that MDPET outperforms the best previously published direct neural network methods in measures of bias, signal-to-noise ratio, mean absolute error, and structural similarity.

Keywords: deep learning, image reconstruction, machine learning, neural network, positron emission tomography

Procedia PDF Downloads 100

Authors: Endrick Barnacin, Jean-Luc Henry, Jack Molinié, Jimmy Nagau, Hélène Delatte, Gérard Lebreton

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Palynology is a field of interest for many disciplines. It has multiple applications such as chronological dating, climatology, allergy treatment, and even honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time-consuming task that requires the intervention of experts in the field, which is becoming increasingly rare due to economic and social conditions. So, the automation of this task is a necessity. Pollen slides analysis is mainly a visual process as it is carried out with the naked eye. That is the reason why a primary method to automate palynology is the use of digital image processing. This method presents the lowest cost and has relatively good accuracy in pollen retrieval. In this work, we propose a system combining recognition and grouping of pollen. It consists of using a Logistic Model Tree to classify pollen already known by the proposed system while detecting any unknown species. Then, the unknown pollen species are divided using a cluster-based approach. Success rates for the recognition of known species have been achieved, and automated clustering seems to be a promising approach.

Keywords: pollen recognition, logistic model tree, expectation-maximization, local binary pattern

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Authors: Sunghun Jung, Wonkook Kim

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Due to the difficulty of the utilization of satellite and an aircraft, conventional ocean color remote sensing has a disadvantage in that it is difficult to obtain images of desired places at desired times. These disadvantages make it difficult to capture the anomalies such as the occurrence of the red tide which requires immediate observation. It is also difficult to understand the phenomena such as the resuspension-precipitation process of suspended solids and the spread of low-salinity water originating in the coastal areas. For the remote sensing reflectance of seawater, above-water radiometers (AWR) have been used either by carrying portable AWRs on a ship or installing those at fixed observation points on the Ieodo ocean research station, Socheongcho base, and etc. In particular, however, it requires the high cost to measure the remote reflectance in various seawater environments at various times and it is even not possible to measure it at the desired frequency in the desired sea area at the desired time. Also, in case of the stationary observation, it is advantageous that observation data is continuously obtained, but there is the disadvantage that data of various sea areas cannot be obtained. It is possible to instantly capture various marine phenomena occurring on the coast using the unmanned aerial system (UAS) including vertical takeoff and landing (VTOL) type unmanned aerial vehicles (UAV) since it could move and hover at the one location and acquire data of the desired form at a high resolution. To remotely estimate seawater constituents, it is necessary to install an ultra-spectral sensor. Also, to calculate reflected light from the surface of the sea in consideration of the sun’s incident light, a total of three sensors need to be installed on the UAV. The remote sensing reflectance of seawater is the most basic optical property for remotely estimating color components in seawater and we could remotely estimate the chlorophyll concentration, the suspended solids concentration, and the dissolved organic amount. Estimating seawater physics from the remote sensing reflectance requires the algorithm development using the accumulation data of seawater reflectivity under various seawater and atmospheric conditions. The UAS with three AWRs is developed for the remote reflection sensing on the surface of the sea. Throughout the paper, we explain the details of each UAS component, system operation scenarios, and simulation and experiment results. The UAS consists of a UAV, a solar tracker, a transmitter, a ground control station (GCS), three AWRs, and two gimbals.

Keywords: above-water radiometers (AWR), ground control station (GCS), unmanned aerial system (UAS), unmanned aerial vehicle (UAV)

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Authors: Rahul Mohite, Lahcen Ouarbya

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This paper focuses on the interplay between humans and computer systems and the ability of these systems to understand and respond to human emotions, including non-verbal communication. Current emotion recognition systems are based solely on either facial or verbal expressions. The limitation of these systems is that it requires large training data sets. The paper proposes a system for recognizing human emotions that combines both speech and emotion recognition. The system utilizes advanced techniques such as deep learning and image recognition to identify facial expressions and comprehend emotions. The results show that the proposed system, based on the combination of facial expression and speech, outperforms existing ones, which are based solely either on facial or verbal expressions. The proposed system detects human emotion with an accuracy of 86%, whereas the existing systems have an accuracy of 70% using verbal expression only and 76% using facial expression only. In this paper, the increasing significance and demand for facial recognition technology in emotion recognition are also discussed.

Keywords: facial reputation, expression reputation, deep gaining knowledge of, photo reputation, facial technology, sign processing, photo type

Procedia PDF Downloads 104

Authors: Titik Wahyuningsih

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The folktale to discuss is Ande-Ande Lumut, a story that is believed to be a history of two kingdoms in East Java, Indonesia. The title refers to the main male character in the story. This research is a library research which aims to know the patriarchal values in Indonesia. The data for the research is the song in the story that is actually the conversation between Ande-Ande Lumut and a mom who adopts him. It is told in the lines that many beautiful girls come to propose Ande-Ande Lumut but he does not want to accept them and keeps on staying in his upstairs room. Finally, he says yes for Klething Kuning to whom his mom describes as a girl with ugly face. Ande-Ande Lumut's decision is taken as Klething Kuning is the only girl who doesn't let Yuyu Kangkang help her. Yuyu Kangkang is described as a very big crab that helps the girls to cross the river but ask them to kiss him. Through the lense of feminist approach, Ande-Ande Lumut shows the men’s preference and dominance to make final decision over women. Even though the girls are actively propose their future husband, but they do it without giving any requirements. Meanwhile, the future husband chooses a girl with a criterion that no male has ever touched her, although the male is a crab.

Keywords: future spouse, Indonesian folktale, acceptance, patriarchal

Procedia PDF Downloads 287

Authors: Ugo Galvanetto, Pirto G. Pavan, Mirco Zaccariotto

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The discipline of Computer-integrated surgery (CIS) will provide equipment able to improve the efficiency of healthcare systems and, which is more important, clinical results. Surgeons and machines will cooperate in new ways that will extend surgeons’ ability to train, plan and carry out surgery. Patient specific CIS of the brain requires several steps: 1 - Fast generation of brain models. Based on image recognition of MR images and equipped with artificial intelligence, image recognition techniques should differentiate among all brain tissues and segment them. After that, automatic mesh generation should create the mathematical model of the brain in which the various tissues (white matter, grey matter, cerebrospinal fluid …) are clearly located in the correct positions. 2 – Reliable and fast simulation of the surgical process. Computational mechanics will be the crucial aspect of the entire procedure. New algorithms will be used to simulate the mechanical behaviour of cutting through cerebral tissues. 3 – Real time provision of visual and haptic feedback A sophisticated human-machine interface based on ergonomics and psychology will provide the feedback to the surgeon. The present work will address in particular point 2. Modelling the cutting of soft tissue in a structure as complex as the human brain is an extremely challenging problem in computational mechanics. The finite element method (FEM), that accurately represents complex geometries and accounts for material and geometrical nonlinearities, is the most used computational tool to simulate the mechanical response of soft tissues. However, the main drawback of FEM lies in the mechanics theory on which it is based, classical continuum Mechanics, which assumes matter is a continuum with no discontinuity. FEM must resort to complex tools such as pre-defined cohesive zones, external phase-field variables, and demanding remeshing techniques to include discontinuities. However, all approaches to equip FEM computational methods with the capability to describe material separation, such as interface elements with cohesive zone models, X-FEM, element erosion, phase-field, have some drawbacks that make them unsuitable for surgery simulation. Interface elements require a-priori knowledge of crack paths. The use of XFEM in 3D is cumbersome. Element erosion does not conserve mass. The Phase Field approach adopts a diffusive crack model instead of describing true tissue separation typical of surgical procedures. Modelling discontinuities, so difficult when using computational approaches based on classical continuum Mechanics, is instead easy for novel computational methods based on Peridynamics (PD). PD is a non-local theory of mechanics formulated with no use of spatial derivatives. Its governing equations are valid at points or surfaces of discontinuity, and it is, therefore especially suited to describe crack propagation and fragmentation problems. Moreover, PD does not require any criterium to decide the direction of crack propagation or the conditions for crack branching or coalescence; in the PD-based computational methods, cracks develop spontaneously in the way which is the most convenient from an energy point of view. Therefore, in PD computational methods, crack propagation in 3D is as easy as it is in 2D, with a remarkable advantage with respect to all other computational techniques.

Keywords: computational mechanics, peridynamics, finite element, biomechanics

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Authors: M. Pomianek, M. Piszczek, M. Maciejewski

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The binocular eye tracking technology is increasingly being used in industry, entertainment and marketing analysis. In the case of virtual reality, eye tracking systems are already the basis for user interaction with the environment. In such systems, the high accuracy of determining the user's eye fixation point is very important due to the specificity of the virtual reality head-mounted display (HMD). Often, however, there are unknown errors occurring in the used eye tracking technology, as well as those resulting from the positioning of the devices in relation to the user's eyes. However, can the virtual environment itself influence estimation errors? The paper presents mathematical analyses and empirical studies of the determination of the fixation point and errors resulting from the change in the size of the pupil in response to the intensity of the displayed scene. The article contains both static laboratory tests as well as on the real user. Based on the research results, optimization solutions were proposed that would reduce the errors of gaze estimation errors. Studies show that errors in estimating the fixation point of vision can be minimized both by improving the pupil positioning algorithm in the video image and by using more precise methods to calibrate the eye tracking system in three-dimensional space.

Keywords: eye tracking, fixation point, pupil size, virtual reality

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Authors: Marcell Serra de Almeida Martins, Benedito de Souza Ribeiro Neto, Gerson Lima Serejo, Carlos Gustavo Resque Dos Santos

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Indoor positioning systems use sensors such as Bluetooth, ZigBee, and Wi-Fi, as well as cameras for image capture, which can be fixed or mobile. These computer vision-based positioning approaches are low-cost to implement, mainly when it uses a mobile camera. The present study aims to create a design of a fiducial marker for a low-cost indoor localization system. The marker is tape-shaped to perform a continuous reading employing two detection algorithms, one for greater distances and another for smaller distances. Therefore, the location service is always operational, even with variations in capture distance. A minimal localization and reading algorithm were implemented for the proposed marker design, aiming to validate it. The accuracy tests consider readings varying the capture distance between [0.5, 10] meters, comparing the proposed marker with others. The tests showed that the proposed marker has a broader capture range than the ArUco and QRCode, maintaining the same size. Therefore, reducing the visual pollution and maximizing the tracking since the ambient can be covered entirely.

Keywords: multiscale recognition, indoor localization, tape-shaped marker, fiducial marker

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Authors: M. L. Gaol, I. W. Mudita

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Indonesia is well known as a country covered by lush tropical rain forests, but in fact, the northeastern part of the country, within the areas geologically known as Lesser Sunda, the dominant vegetation is tropical savanna. Lesser Sunda is a chain of islands located closer to Australia than to islands in the other parts of the country. Among those of islands in the chain which is closes to Australia, and thereby most strongly affected by the hot and dry Australian climate, is the island of Timor, the western part of which belongs to Indonesia and the eastern part is a sovereign state East Timor. Regardless of being the most dominant vegetation cover, tropical savanna in West Timor, especially its understory, is rarely investigated. This research was therefore carried out to investigate the structure, composition and diversity of the understory of this tropical savanna as the basis for looking at the possibility of introducing other spesieis for various purposes. For this research, 14 terrestrial communities representing major types of the existing savannas in West Timor was selected with aid of the most recently available satellite imagery. At each community, one stand of the size of 50 m x 50 m most likely representing the community was as the site of observation for the type of savanna under investigation. At each of the 14 communities, 20 plots of 1 m x 1 m in size was placed at random to identify understory species and to count the total number of individuals and to estimate the cover of each species. Based on such counts and estimation, the important value of each species was later calculated. The results of this research indicated that the understory of savanna in West Timor consisted of 73 understory species. Of this number of species, 18 species are grasses and 55 are non-grasses. Although lower than non-grass species, grass species indeed dominated the savanna as indicated by their number of individuals (65.33 vs 34.67%), species cover (57.80 vs 42.20%), and important value (123.15 vs 76.85). Of the 14 communities, the lowest density of grass was 13.50/m2 and the highest was 417.50/m2. Of 18 grass species found, all were commonly found as agricultural weeds, whereas of 55 non-grass, 10 species were commonly found as agricultural weeds, environmental weeds, or invasive species. In terms of better managing the savanna in the region, these findings provided the basis for planning a more integrated approach in managing such agricultural and environmental weeds as well as invasive species by considering the structure, composition, and species diversity of the understory species existing in each site. These findings also provided the basis for better understanding the flora of the region as a whole and for developing a flora database of West Timor in future.

Keywords: tropical savanna, understory species, integrated management, weedy and invasive species

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Authors: Jaime E. Salazar Lagos

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Chongqing is located in southwest China and is becoming one of the most significant cities in the world. Its urban territories and metropolitan-related areas have one of the largest urban populations in China and are partitioned and shaped by two of the biggest and longest rivers on Earth, the Yangtze and Jialing Rivers, making Chongqing a megalopolis intersected by rivers. Historically, Chongqing City enjoyed fundamental connections with its rivers; however, current urban development of Chongqing City has lost effective integration of the riverbanks within the urban space and structural dynamics of the city. Therefore, there exists a critical lack of physical and urban space conjoined with the rivers, which diminishes the economic, tourist, and environmental development of Chongqing. Using multi-scale satellite-map site verification the study confirmed the hypothesis and urban-waterside disconnect. Collected data demonstrated that the Chongqing urban zone, an area of 5292 square-kilometers and a water front of 203.4 kilometers, has only 23.49 kilometers of extension (just 11.5%) with high-quality physical and spatial urban-waterside connection. Compared with other metropolises around the world, this figure represents a significant lack of spatial development along the rivers, an issue that has not been successfully addressed in the last 10 years of urban development. On a macro scale, the study categorized the different kinds of relationships between the city and its riverbanks. This data was then utilized in the creation of an urban-waterfront relationship map that can be a tool for future city planning decisions and real estate development. On a micro scale, we discovered there are three primary elements that are causing the urban-waterside disconnect: extensive highways along the most dense areas and city center, large private real estate developments that do not provide adequate riverside access, and large industrial complexes that almost completely lack riverside utilization. Finally, as part of the suggested strategies, the study concludes that the most efficient and practical way to improve this situation is to follow the historic master-planning of Chongqing and create connective nodes in critical urban locations along the river, a strategy that has been used for centuries to handle the same urban-waterside relationship. Reviewing and implementing this strategy will allow the city to better connect with the rivers, reducing the various impacts of disconnect and urban transformation.

Keywords: Chongqing City, megalopolis, nodes, riverbanks disconnection, urban

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Authors: Reza Mohammadi, Mahmod R. Sahebi, Mehrnoosh Omati, Milad Vahidi

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Classification of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images plays an important role in land cover and land use management. Recently, classification algorithms based on Bag of Visual Words (BOVW) model have attracted significant interest among scholars and researchers in and out of the field of remote sensing. In this paper, BOVW model with pixel based low-level features has been implemented to classify a subset of San Francisco bay PolSAR image, acquired by RADARSAR 2 in C-band. We have used segment-based decision-making strategy and compared the result with the result of traditional Support Vector Machine (SVM) classifier. 90.95% overall accuracy of the classification with the proposed algorithm has shown that the proposed algorithm is comparable with the state-of-the-art methods. In addition to increase in the classification accuracy, the proposed method has decreased undesirable speckle effect of SAR images.

Keywords: Bag of Visual Words (BOVW), classification, feature extraction, land cover management, Polarimetric Synthetic Aperture Radar (PolSAR)

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Authors: Azimollah Aleshzadeh, Enver Vural Yavuz

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The main goal of this study is to produce landslide susceptibility maps using different data-driven bivariate statistical approaches; namely, entropy weight method (EWM), evidence belief function (EBF), and information content model (ICM), at Uzundere county, Erzurum province, in the north-eastern part of Turkey. Past landslide occurrences were identified and mapped from an interpretation of high-resolution satellite images, and earlier reports as well as by carrying out field surveys. In total, 42 landslide incidence polygons were mapped using ArcGIS 10.4.1 software and randomly split into a construction dataset 70 % (30 landslide incidences) for building the EWM, EBF, and ICM models and the remaining 30 % (12 landslides incidences) were used for verification purposes. Twelve layers of landslide-predisposing parameters were prepared, including total surface radiation, maximum relief, soil groups, standard curvature, distance to stream/river sites, distance to the road network, surface roughness, land use pattern, engineering geological rock group, topographical elevation, the orientation of slope, and terrain slope gradient. The relationships between the landslide-predisposing parameters and the landslide inventory map were determined using different statistical models (EWM, EBF, and ICM). The model results were validated with landslide incidences, which were not used during the model construction. In addition, receiver operating characteristic curves were applied, and the area under the curve (AUC) was determined for the different susceptibility maps using the success (construction data) and prediction (verification data) rate curves. The results revealed that the AUC for success rates are 0.7055, 0.7221, and 0.7368, while the prediction rates are 0.6811, 0.6997, and 0.7105 for EWM, EBF, and ICM models, respectively. Consequently, landslide susceptibility maps were classified into five susceptibility classes, including very low, low, moderate, high, and very high. Additionally, the portion of construction and verification landslides incidences in high and very high landslide susceptibility classes in each map was determined. The results showed that the EWM, EBF, and ICM models produced satisfactory accuracy. The obtained landslide susceptibility maps may be useful for future natural hazard mitigation studies and planning purposes for environmental protection.

Keywords: entropy weight method, evidence belief function, information content model, landslide susceptibility mapping

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Authors: Belayneh Matebie, Michael Melese

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The study focuses on addressing the challenges in classifying and ensuring the quality of Eragrostis Teff, a small and round grain that is the smallest cereal grain. Employing a traditional classification method is challenging because of its small size and the similarity of its environmental characteristics. To overcome this, this study employs a machine learning approach to develop a source and quality classification system for Teff cereal. Data is collected from various production areas in the Amhara regions, considering two types of cereal (high and low quality) across eight classes. A total of 5,920 images are collected, with 740 images for each class. Image enhancement techniques, including scaling, data augmentation, histogram equalization, and noise removal, are applied to preprocess the data. Convolutional Neural Network (CNN) is then used to extract relevant features and reduce dimensionality. The dataset is split into 80% for training and 20% for testing. Different classifiers, including FVGG16, FINCV3, QSCTC, EMQSCTC, SVM, and RF, are employed for classification, achieving accuracy rates ranging from 86.91% to 97.72%. The ensemble of FVGG16, FINCV3, and QSCTC using the Max-Voting approach outperforms individual algorithms.

Keywords: Teff, ensemble learning, max-voting, CNN, SVM, RF

Procedia PDF Downloads 37