Search results for: aerial images

Authors: Sylvester Akpah, Selasi Vondee

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Unmanned Aerial Vehicles (UAV) as they are also known, drones have attracted increasing attention in recent years due to their ubiquitous nature and boundless applications in the areas of communication, surveying, aerial photography, weather forecasting, medical delivery, surveillance amongst others. Operated remotely in real-time or pre-programmed, drones can fly autonomously or on pre-defined routes. The application of these aerial vehicles has successfully penetrated the world due to technological evolution, thus a lot more businesses are utilizing their capabilities. Unfortunately, while drones are replete with the benefits stated supra, they are riddled with some problems, mainly attributed to the complexities in learning how to master drone flights, collision avoidance and enterprise security. Additional challenges, such as the analysis of flight data recorded by sensors attached to the drone may take time and require expert help to analyse and understand. This paper presents an autonomous drone control system using a chatbot. The system allows for easy control of drones using conversations with the aid of Natural Language Processing, thus to reduce the workload needed to set up, deploy, control, and monitor drone flight missions. The results obtained at the end of the study revealed that the drone connected to the chatbot was able to initiate flight missions with just text and voice commands, enable conversation and give real-time feedback from data and requests made to the chatbot. The results further revealed that the system was able to process natural language and produced human-like conversational abilities using Artificial Intelligence (Natural Language Understanding). It is recommended that radio signal adapters be used instead of wireless connections thus to increase the range of communication with the aerial vehicle.

Keywords: artificial ntelligence, chatbot, natural language processing, unmanned aerial vehicle

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Authors: Getaneh Berie Tarekegn

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Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. With reconfigurable intelligent surfaces (RIS), this problem can be effectively addressed. To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communication system. According to simulation results, LSTM can improve the channel estimation performance of RIS-assisted UAV-enabled wireless communication.

Keywords: channel estimation, reconfigurable intelligent surfaces, long short-term memory, unmanned aerial vehicles

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Authors: Aissa Belmeguenai, Khaled Mansouri

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In this work, an efficient implementation of Achterbahn-128 for images encryption and decryption was introduced. The implementation for this simulated project is written by MATLAB.7.5. At first two different original images are used for validate the proposed design. Then our developed program was used to transform the original images data into image digits file. Finally, we used our implemented program to encrypt and decrypt images data. Several tests are done for proving the design performance including visual tests and security analysis; we discuss the security analysis of the proposed image encryption scheme including some important ones like key sensitivity analysis, key space analysis, and statistical attacks.

Keywords: Achterbahn-128, stream cipher, image encryption, security analysis

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Authors: A. Kavya Sri, K. Sai Vineela, R. Vanitha, S. Rohith

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Large pieces of ice that break from the glaciers are known as icebergs. The threat that icebergs pose to navigation, production of offshore oil and gas services, and underwater pipelines makes their detection crucial. In this project, an automated iceberg tracking method using deep learning techniques and satellite images of icebergs is to be developed. With a temporal resolution of 12 days and a spatial resolution of 20 m, Sentinel-1 (SAR) images can be used to track iceberg drift over the Southern Ocean. In contrast to multispectral images, SAR images are used for analysis in meteorological conditions. This project develops a web-based graphical user interface to detect and track icebergs using sentinel-1 images. To track the movement of the icebergs by using temporal images based on their latitude and longitude values and by comparing the center and area of all detected icebergs. Testing the accuracy is done by precision and recall measures.

Keywords: synthetic aperture radar (SAR), icebergs, deep learning, spatial resolution, temporal resolution

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Authors: Nikola Vlahovic, Blazenka Knezevic, Petra Batalic

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In this paper, we will present a research about feasibility of implementing unmanned aerial vehicles, also known as 'drones', in logistics. Research is based on available information about current incentives and experiments in application of delivery drones in commercial use. Overview of current pilot projects and literature, as well as an overview of detected challenges, will be compiled and presented. Based on these findings, we will present a conceptual model of business process that implements delivery drones in business to business logistic operations. Business scenario is based on a pharmaceutical supply chain. Simulation modeling will be used to create models for running experiments and collecting performance data. Comparative study of the presented conceptual model will be given. The work will outline the main advantages and disadvantages of implementing unmanned aerial vehicles in delivery services as a supplementary distribution channel along the supply chain.

Keywords: business process, delivery drones, logistics, simulation modelling, unmanned aerial vehicles

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Authors: Chaitanya Chawla, Divya Panwar, Gurneesh Singh Anand, M. P. S Bhatia

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This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.

Keywords: image forensics, computer graphics, classification, deep learning, convolutional neural networks

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Authors: Toufic Abd El-Latif Sadek

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The Asphalt object represents the asphalted areas, like roads. The best original data of thermal images occurred at a specific time during the days of the year, by preventing the gaps in times which give the close and same brightness from different objects, using seven sample objects, asphalt, concrete, metal, rock, dry soil, vegetation, and water. It has been found in this study a general timing equation for capturing satellite thermal images at different locations, depends on a fixed time the sunrise and sunset; Capture Time= Tcap =(TM*TSR) ±TS.

Keywords: asphalt, satellite, thermal images, timing equation

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Authors: Aisulu Tileukulova, Zhexebay Dauren

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Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.

Keywords: radar, neural network, convolutional neural network, echo signals

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Authors: R. Schurig, T. Désesquelles, A. Dumont, E. Lefranc, A. Lux

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A cost-efficient alternative is proposed to the use of a single drone carrying multiple cameras in order to take stereoscopic images and videos during its flight. Such drone has to be particularly large enough to take off with its equipment, and stable enough in order to make valid measurements. Corresponding performance for a single aircraft usually comes with a large cost. Proposed solution consists in using multiple smaller and cheaper aircrafts carrying one camera each instead of a single expensive one. To give a proof of concept, AR drones, quad-rotor UAVs from Parrot Inc., are experimentally used.

Keywords: drone squadron, flight control, rotorcraft, Unmanned Aerial Vehicle (UAV), AR drone, stereoscopic vision

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Authors: Alia Ragheb

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Philodendron species were reported in traditional medicine for the treatment of several diseases. From the 70% methanol extract of the aerial parts of Philodendron bipinnatifidum Schott ex Endl, nine flavonoid compounds were isolated and identified for the first time; saponarin, genkwanin 8-C-(2′′-O-β-glucopyranosyl)-β-glucopyranoside, apigenin 6-C-(2′′-O-β-glucopyranosyl)-β-glucopyranoside, schaftoside, swertisin, swertiajaponin, isoswertisin, isorhamnetin 3-O-(2′′-acetyl)-β-glucopyranoside and apigenin. Characterization of the plant was achieved using chromatographic, physical, chemical, spectroscopic, and spectrometric techniques. The 70% methanol aerial parts extract and the methanol fraction of the plant were in vivo screened for their acute anti-inflammatory, antipyretic and analgesic effects where significant effects were exhibited compared to that of reference drugs. From the reported literature, these biological activities could be attributed to its phenolic constituent. The 70% methanol aerial parts and successive extracts, as well as some pure isolated flavonoid compounds, were in vitro investigated for their antioxidant, antimicrobial and cytotoxic activities.

Keywords: antioxidant, araceae, cytotoxicity, flavonoids

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Authors: Tugrul Oktay, Mehmet Konar, Mohamed Abdallah Mohamed, Murat Aydin, Firat Sal, Murat Onay, Mustafa Soylak

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In this paper, it is aimed to improve autonomous flight performance of a load-carrying (payload: 3 kg and total: 6kg) unmanned aerial vehicle (UAV) through active wing and horizontal tail active morphing and also integrated autopilot system parameters (i.e. P, I, D gains) and UAV parameters (i.e. extension ratios of wing and horizontal tail during flight) design. For this purpose, a loadcarrying UAV (i.e. ZANKA-II) is manufactured in Erciyes University, College of Aviation, Model Aircraft Laboratory is benefited. Optimum values of UAV parameters and autopilot parameters are obtained using a stochastic optimization method. Using this approach autonomous flight performance of UAV is substantially improved and also in some adverse weather conditions an opportunity for safe flight is satisfied. Active morphing and integrated design approach gives confidence, high performance and easy-utility request of UAV users.

Keywords: unmanned aerial vehicles, morphing, autopilots, autonomous performance

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Authors: Bikshalu Kalagadda, Srikanth Rangu

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The important object of images segmentation is to extract objects with respect to some input features. One of the important methods for image segmentation is Level set method. Generally medical images and synthetic images with low contrast of pixel profile, for such images difficult to locate interested features in images. In conventional level set function, develops irregularity during its process of evaluation of contour of objects, this destroy the stability of evolution process. For this problem a remedy is proposed, a new hybrid algorithm is Clustering Level Set Evolution. Kernel fuzzy particles swarm optimization clustering with the Distance Regularized Level Set (DRLS) and Selective Binary, and Gaussian Filtering Regularized Level Set (SBGFRLS) methods are used. The ability of identifying different regions becomes easy with improved speed. Efficiency of the modified method can be evaluated by comparing with the previous method for similar specifications. Comparison can be carried out by considering medical and synthetic images.

Keywords: segmentation, clustering, level set function, re-initialization, Kernel fuzzy, swarm optimization

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Authors: Adnan A. Y. Mustafa

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Image matching is a fundamental problem that arises frequently in many aspects of robot and computer vision. It can become a time-consuming process when matching images to a database consisting of hundreds of images, especially if the images are big. One approach to reducing the time complexity of the matching process is to reduce the search space in a pre-matching stage, by simply removing dissimilar images quickly. The Probabilistic Matching Model for Binary Images (PMMBI) showed that dissimilarity detection between binary images can be accomplished quickly by random pixel mapping and is size invariant. The model is based on the gamma binary similarity distance that recognizes an image and its inverse as containing the same scene and hence considers them to be the same image. However, in many applications, an image and its inverse are not treated as being the same but rather dissimilar. In this paper, we present a comparative analysis of dissimilarity detection between PMMBI based on the gamma binary similarity distance and a modified PMMBI model based on a similarity distance that does distinguish between an image and its inverse as being dissimilar.

Keywords: binary image, dissimilarity detection, probabilistic matching model for binary images, image mapping

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Authors: Amhimmid Mohammed Saffour, Mustafa Mohamed Abdullah

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The use of computers for handling image data in the healthcare is growing. However, the amount of data produced by modern image generating techniques is vast. This data might be a problem from a storage point of view or when the data is sent over a network. This paper using wavelet transform technique for medical images compression. MATLAB program, are designed to evaluate medical images storage and transmission time problem at Sebha Medical Center Libya. In this paper, three different Computed Tomography images which are abdomen, brain and chest have been selected and compressed using wavelet transform. Objective evaluation has been performed to measure the quality of the compressed images. For this evaluation, the results show that the Peak Signal to Noise Ratio (PSNR) which indicates the quality of the compressed image is ranging from (25.89db to 34.35db for abdomen images, 23.26db to 33.3db for brain images and 25.5db to 36.11db for chest images. These values shows that the compression ratio is nearly to 30:1 is acceptable.

Keywords: medical image, Matlab, image compression, wavelet's, objective evaluation

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Authors: C. G. Jinimole, A. Harsha

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One of the key problems facing in the analysis of Computed Tomography (CT) images is the poor contrast of the images. Image enhancement can be used to improve the visual clarity and quality of the images or to provide a better transformation representation for further processing. Contrast enhancement of images is one of the acceptable methods used for image enhancement in various applications in the medical field. This will be helpful to visualize and extract details of brain infarctions, tumors, and cancers from the CT image. This paper presents a comparison study of five contrast enhancement techniques suitable for the contrast enhancement of CT images. The types of techniques include Power Law Transformation, Logarithmic Transformation, Histogram Equalization, Contrast Stretching, and Laplacian Transformation. All these techniques are compared with each other to find out which enhancement provides better contrast of CT image. For the comparison of the techniques, the parameters Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are used. Logarithmic Transformation provided the clearer and best quality image compared to all other techniques studied and has got the highest value of PSNR. Comparison concludes with better approach for its future research especially for mapping abnormalities from CT images resulting from Brain Injuries.

Keywords: computed tomography, enhancement techniques, increasing contrast, PSNR and MSE

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Authors: Ankit Sharma, Mudit Sachan, Anurag Prakash

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Rapid growth in data acquisition technologies through drones, have led to advances and interests in collecting high-resolution images of geological fields. Being advantageous in capturing high volume of data in short flights, a number of challenges have to overcome for efficient analysis of this data, especially while data acquisition, image interpretation and processing. We introduce a method that allows effective mapping of geological fields using photogrammetric data of surfaces, drainage area, water bodies etc, which will be captured by airborne vehicles like UAVs, we are not taking satellite images because of problems in adequate resolution, time when it is captured may be 1 yr back, availability problem, difficult to capture exact image, then night vision etc. This method includes advanced automated image interpretation technology and human data interaction to model structures and. First Geological structures will be detected from the primary photographic dataset and the equivalent three dimensional structures would then be identified by digital elevation model. We can calculate dip and its direction by using the above information. The structural map will be generated by adopting a specified methodology starting from choosing the appropriate camera, camera’s mounting system, UAVs design ( based on the area and application), Challenge in air borne systems like Errors in image orientation, payload problem, mosaicing and geo referencing and registering of different images to applying DEM. The paper shows the potential of using our method for accurate and efficient modeling of geological structures, capture particularly from remote, of inaccessible and hazardous sites.

Keywords: digital elevation model, mapping, photogrammetric data analysis, geological structures

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Authors: Andrew Lock, Alastair Allen

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Fragile watermarking has been proposed as a means of adding additional security or functionality to biometric systems, particularly for authentication and tamper detection. In this paper we describe an experimental study on the effect of watermarking iris images with a particular class of fragile algorithm, reversible algorithms, and the ability to correctly perform iris recognition. We investigate two scenarios, matching watermarked images to unmodified images, and matching watermarked images to watermarked images. We show that different watermarking schemes give very different results for a given capacity, highlighting the importance of investigation. At high embedding rates most algorithms cause significant reduction in recognition performance. However, in many cases, for low embedding rates, recognition accuracy is improved by the watermarking process.

Keywords: biometrics, iris recognition, reversible watermarking, vision engineering

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Authors: Nidhal Azawi

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Colonoscopy procedure is widely used in the world to detect an abnormality. Early diagnosis can help to heal many patients. Because of the unavoidable artifacts that exist in colon images, doctors cannot detect a colon surface precisely. The purpose of this work is to improve the visual quality of colonoscopy videos to provide better information for physicians by removing some artifacts. This work complements a series of work consisting of three previously published papers. In this paper, Optic flow is used for motion compensation, and then consecutive images are aligned/registered to integrate some information to create a new image that has or reveals more information than the original one. Colon images have been classified into informative and noninformative images by using a deep neural network. Then, two different strategies were used to treat informative and noninformative images. Informative images were treated by using Lucas Kanade (LK) with an adaptive temporal mean/median filter, whereas noninformative images are treated by using Lucas Kanade with a derivative of Gaussian (LKDOG) with adaptive temporal median images. A comparison result showed that this work achieved better results than that results in the state- of- the- art strategies for the same degraded colon images data set, which consists of 1000 images. The new proposed algorithm reduced the error alignment by about a factor of 0.3 with a 100% successfully image alignment ratio. In conclusion, this algorithm achieved better results than the state-of-the-art approaches in case of enhancing the informative images as shown in the results section; also, it succeeded to convert the non-informative images that have very few details/no details because of the blurriness/out of focus or because of the specular highlight dominate significant amount of an image to informative images.

Keywords: optic flow, colonoscopy, artifacts, spatial temporal filter

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Authors: Lucas Barbosa Da Silva, Jun Okamoto Jr.

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Precision Agriculture has long being benefited from crop fields’ aerial imagery. This important tool has allowed identifying patterns in crop fields, generating useful information to the production management. Reflectance intensity data in different ranges from the electromagnetic spectrum may indicate presence or absence of nutrients in the soil of an area. Different relations between the different light bands may generate even more detailed information. The knowledge of the nutrients content in the soil or in the crop during its growth is a valuable asset to the farmer that seeks to optimize its yield. However, small farmers in Brazil often lack the resources to access this kind information, and, even when they do, it is not presented in a comprehensive and/or objective way. So, the challenges of implementing this technology ranges from the sampling of the imagery, using aerial platforms, building of a mosaic with the images to cover the entire crop field, extracting the reflectance information from it and analyzing its relationship with the parameters of interest, to the display of the results in a manner that the farmer may take the necessary decisions more objectively. In this work, it’s proposed an analysis of soil nutrient contents based on image processing of satellite imagery and comparing its outtakes with commercial laboratory’s chemical analysis. Also, sources of satellite imagery are compared, to assess the feasibility of using Google Earth data in this application, and the impacts of doing so, versus the application of imagery from satellites like Landsat-8 and Pleiades. Furthermore, an algorithm for building mosaics is implemented using Google Earth imagery and finally, the possibility of using unmanned aerial vehicles is analyzed. From the data obtained, some soil parameters are estimated, namely, the content of Potassium, Phosphorus, Boron, Manganese, among others. The suitability of Google Earth Imagery for this application is verified within a reasonable margin, when compared to Pleiades Satellite imagery and to the current commercial model. It is also verified that the mosaic construction method has little or no influence on the estimation results. Variability maps are created over the covered area and the impacts of the image resolution and sample time frame are discussed, allowing easy assessments of the results. The final results show that easy and cheaper remote sensing and analysis methods are possible and feasible alternatives for the small farmer, with little access to technological and/or financial resources, to make more accurate decisions about soil nutrient management.

Keywords: remote sensing, precision agriculture, mosaic, soil, nutrient content, satellite imagery, aerial imagery

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Authors: Aymen Bouzid, Jihen Ben Smida

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

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

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Authors: Kalaivani Pachiappan, Sabari Annaji, Nithya Jayakumar

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In recent years, Information security has emerged as foremost challenges in many fields. Especially in medical information systems security is a major issue, in handling reports such as patients’ diagnosis and medical images. These sensitive data require confidentiality for transmission purposes. Image sharing is a secure and fault-tolerant method for protecting digital images, which can use the cryptography techniques to reduce the information loss. In this paper, visual sharing method is proposed which embeds the patient’s details into a medical image. Then the medical image can be divided into numerous shared images and protected by various users. The original patient details and medical image can be retrieved by gathering the shared images.

Keywords: information security, medical images, cryptography, visual sharing

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Authors: A. Benbouali, H. Saidi, A. Derrouazin, T. Bessaad

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Aerial robotics is a very exciting research field dealing with a variety of subjects, including the attitude control. This paper deals with the control of a four rotor vertical take-off and landing (VTOL) Unmanned Aerial Vehicle. The paper presents a mathematical model based on the approach of Lagrange for the flight control of an autonomous quad-rotor. It also describes the controller architecture which is based on PID regulators. The control method has been simulated in closed loop in different situations. All the calculation stages and the simulation results have been detailed.

Keywords: quad-rotor, lagrange approach, proportional integral derivate (PID) controller, Matlab/Simulink

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Authors: Mengmeng Du, Noboru Noguchi, Hiroshi Okamoto, Noriko Kobayashi

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This paper introduced a topographic mapping system with time-saving and simplicity advantages based on integration of Light Detection and Ranging (LiDAR) data and Post Processing Kinematic Global Positioning System (PPK GPS) data. This topographic mapping system used a low-altitude Unmanned Aerial Vehicle (UAV) as a platform to conduct land survey in a low-cost, efficient, and totally autonomous manner. An experiment in a small-scale sugarcane farmland was conducted in Queensland, Australia. Subsequently, we synchronized LiDAR distance measurements that were corrected by using attitude information from gyroscope with PPK GPS coordinates for generation of precision topographic maps, which could be further utilized for such applications like precise land leveling and drainage management. The results indicated that LiDAR distance measurements and PPK GPS altitude reached good accuracy of less than 0.015 m.

Keywords: land survey, light detection and ranging, post processing kinematic global positioning system, precision agriculture, topographic map, unmanned aerial vehicle

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Authors: Antigoni Panagiotopoulou, Lemonia Ragia

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High-resolution satellite images can provide detailed information about change detection on the earth. In the present work, QuickBird images of spatial resolution 60 cm/pixel and WorldView images of resolution 30 cm/pixel are utilized to perform automatic change detection in urban and suburban areas of Crete, Greece. There is a relative time difference of 13 years among the satellite images. Multiindex scene representation is applied on the images to classify the scene into buildings, vegetation, water and ground. Then, automatic change detection is made possible by pixel-per-pixel comparison of the classified multi-temporal images. The vegetation index and the water index which have been developed in this study prove effective. Furthermore, the proposed change detection approach not only indicates whether changes have taken place or not but also provides specific information relative to the types of changes. Experimentations with other different scenes in the future could help optimize the proposed spectral indices as well as the entire change detection methodology.

Keywords: change detection, multiindex scene representation, spectral index, QuickBird, WorldView

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Authors: Gopi Kandaswamy, P. Balamuralidhar

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Fully autonomous small Unmanned Aerial Vehicles (UAVs) are increasingly being used in many commercial applications. Although a lot of research has been done to develop safe, reliable and durable UAVs, accidents due to electronic and structural failures are not uncommon and pose a huge safety risk to the UAV operators and the public. Hence there is a strong need for an automated health monitoring system for UAVs with a view to minimizing mission failures thereby increasing safety. This paper describes our approach to monitoring the electronic and structural components in a small UAV without the need for additional sensors to do the monitoring. Our system monitors data from four sources; sensors, navigation algorithms, control inputs from the operator and flight controller outputs. It then does statistical analysis on the data and applies a rule based engine to detect failures. This information can then be fed back into the UAV and a decision to continue or abort the mission can be taken automatically by the UAV and independent of the operator. Our system has been verified using data obtained from real flights over the past year from UAVs of various sizes that have been designed and deployed by us for various applications.

Keywords: fault detection, health monitoring, unmanned aerial vehicles, vibration analysis

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Authors: Abdullah Beyazkurk, Onur Ozdemir

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The evolution of warfare has been affected from technological developments to a large extent. Another important factor that affected the evolution of warfare is the space. Technological developments became cornerstones for the organization of the forces on the field, while space of the battlefield gained importance with the introduction of urban areas as 'battlefields'. The use of urban areas as battlefields increased the casualty, while technological developments began to play a remedial role. Thus, the unmanned systems drew attention as the remedy. Today's widely used unmanned aerial vehicles have great effects on the operations. On the other hand, with the increasing urbanization, and the wide use of urban areas as battlefields make it a necessity to benefit from unmanned systems on the ground as well. This study focuses on the use of unmanned aerial systems as well as unmanned ground systems in urban warfare, with regards to their performance and cost affectivity. The study defends that the use of unmanned vehicles will be remedial for increasing casualty rates, while their precision and superhuman capacity will manifest the performance advantage. The findings of this study will help modern armies focus on unmanned systems, especially for the urban, anti-terror, or counter insurgency operations.

Keywords: technology, warfare, urban warfare, unmanned systems, unmanned ground vehicles, unmanned aerial vehicles

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Authors: Koushlendra Kumar Singh, Manish Kumar Bajpai, Rajesh K. Pandey

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The edges of low contrast images are not clearly distinguishable to the human eye. It is difficult to find the edges and boundaries in it. The present work encompasses a new approach for low contrast images. The Chebyshev polynomial based fractional order filter has been used for filtering operation on an image. The preprocessing has been performed by this filter on the input image. Laplacian of Gaussian method has been applied on preprocessed image for edge detection. The algorithm has been tested on two test images.

Keywords: low contrast image, fractional order differentiator, Laplacian of Gaussian (LoG) method, chebyshev polynomial

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Authors: Noriki Amano

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The final goal of this study is to create another way in which people enjoy music through the performance of 'Wearable Music'. Concretely speaking, we generate colorful costumes in real- time from music and to realize their dressing by projecting them to a person. For this purpose, we propose three methods in this study. First, a method of giving color to music in a three-dimensionally way. Second, a method of generating images of costumes from music. Third, a method of wearing the images of music. In particular, this study stands out from other related work in that we generate images of unique costumes from music and realize to wear them. In this study, we use the technique of generative arts to generate images of unique costumes and project the images to the fog generated around a person from 3-way using projectors. From this study, we can get how to enjoy music as 'wearable'. Furthermore, we are also able to have the prospect of unconventional entertainment based on the fusion between music and costumes.

Keywords: entertainment computing, costumes, music, generative programming

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Authors: Agoston Restas

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Introduction: This paper describes many initiatives and shows also practical examples which happened recently using Unmanned Aerial Systems (UAS) to support disaster management. Since the operation of manned aircraft at disasters is usually not only expensive but often impossible to use as well, in many cases managers fail to use the aerial activity. UAS can be an alternative moreover cost-effective solution for supporting disaster management. Methods: This article uses thematic division of UAS applications; it is based on two key elements, one of them is the time flow of managing disasters, other is its tactical requirements. Logically UAS can be used like pre-disaster activity, activity immediately after the occurrence of a disaster and the activity after the primary disaster elimination. Paper faces different disasters, like dangerous material releases, floods, earthquakes, forest fires and human-induced disasters. Research used function analysis, practical experiments, mathematical formulas, economic analysis and also expert estimation. Author gathered international examples and used own experiences in this field as well. Results and discussion: An earthquake is a rapid escalating disaster, where, many times, there is no other way for a rapid damage assessment than aerial reconnaissance. For special rescue teams, the UAS application can help much in a rapid location selection, where enough place remained to survive for victims. Floods are typical for a slow onset disaster. In contrast, managing floods is a very complex and difficult task. It requires continuous monitoring of dykes, flooded and threatened areas. UAS can help managers largely keeping an area under observation. Forest fires are disasters, where the tactical application of UAS is already well developed. It can be used for fire detection, intervention monitoring and also for post-fire monitoring. In case of nuclear accident or hazardous material leakage, UAS is also a very effective or can be the only one tool for supporting disaster management. Paper shows some efforts using UAS to avoid human-induced disasters in low-income countries as part of health cooperation.

Keywords: disaster management, floods, forest fires, Unmanned Aerial Systems

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Authors: Soni Ariawan

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The study aims to explore the cultural diversity highlighted in EFL textbook for Senior High School grade 10 in Indonesia. The visual images are selected as the data and qualitatively analysed using content analysis. The reason to choose visual images because images are not always neutral and they might impact teaching and learning process. In the current study, cultural diversity aspects are focused on religion (Muslim, Protestant, Catholic, Hindu, Buddhist, Confucian), gender (male, female, unclear), ethnic (Melanesian, Austronesian, Foreigner) and socioeconomic (low, middle, high, undetermined) diversity as the theoretical framework. The four aspects of cultural diversity are sufficiently representative to draw a conclusion in investigating Indonesian culture representation in EFL textbook. The finding shows that cultural diversity is not proportionally reflected in the textbook, particularly in the visual images.

Keywords: EFL textbook, cultural diversity, visual images, Indonesia

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