Search results for: image clustering

Authors: Tomáš Dyk, Stanislav Procházka, Martin Drahanský

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The article proposes a new algorithm for detecting damaged areas of the tank barrel based on the image of the inner surface of the tank barrel. Damage position is calculated using image processing techniques such as edge detection, discrete wavelet transformation and image segmentation for accurate contour detection. The algorithm can detect surface damage in smoothbore and even in rifled tank barrels. The algorithm also calculates the volume of the detected damage from the depth map generated, for example, from the distance measurement unit. The proposed method was tested on data obtained by a tank barrel scanning device, which generates both surface image data and depth map. The article also discusses tank barrel scanning devices and how damaged surface impacts material resistance.

Keywords: barrel, barrel diagnostic, image processing, surface damage detection, tank

Procedia PDF Downloads 136

Authors: Anam Naveed, Asma Andleeb, Mehreen Sirshar

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In the article, Image processing techniques have been applied on cardiac images for enhancing the image quality. Two types of methodologies considers for survey, invasive techniques and non-invasive techniques. Different image processes for improvement of cardiac image quality and reduce the amount of radiation exposure for invasive techniques are explored. Different image processing algorithms for enhancing the noninvasive cardiac image qualities are described. Beside these two methodologies, third methodology has applied on live streaming of heart rate on ECG window for extracting necessary information, removing noise and enhancing quality. Sensitivity analyses have been carried out to investigate the impacts of cardiac images for diagnosis of cardiac arteries disease and how the enhancement on images will help the cardiologist to diagnoses disease. The paper evaluates strengths and weaknesses of different techniques applied for improved the image quality and draw a conclusion. Some specific limitations must be considered for whole survey, like the patient heart beat must be 70-75 beats/minute while doing the angiography, similarly patient weight and exposure radiation amount has some limitation.

Keywords: cardiac images, CT angiography, critical analysis, exposure radiation, invasive techniques, invasive techniques, non-invasive techniques

Procedia PDF Downloads 348

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

Procedia PDF Downloads 634

Authors: Azam Fazelpour, Saeed R. Dehghani, Vlastimil Masek, Yuri S. Muzychka

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The study uses an image analysis technique employing thermal imaging to measure the percentage of areas with various temperatures on a freezing surface. An image segmentation method using threshold values is applied to a sequence of image recording the freezing process. The phenomenon is transient and temperatures vary fast to reach the freezing point and complete the freezing process. Freezing salt water is subjected to the salt rejection that makes the freezing point dynamic and dependent on the salinity at the phase interface. For a specific area of freezing, nucleation starts from one side and end to another side, which causes a dynamic and transient temperature in that area. Thermal cameras are able to reveal a difference in temperature due to their sensitivity to infrared radiance. Using Experimental setup, a video is recorded by a thermal camera to monitor radiance and temperatures during the freezing process. Image processing techniques are applied to all frames to detect and classify temperatures on the surface. Image processing segmentation method is used to find contours with same temperatures on the icing surface. Each segment is obtained using the temperature range appeared in the image and correspond pixel values in the image. Using the contours extracted from image and camera parameters, stratified areas with different temperatures are calculated. To observe temperature contours on the icing surface using the thermal camera, the salt water sample is dropped on a cold surface with the temperature of -20°C. A thermal video is recorded for 2 minutes to observe the temperature field. Examining the results obtained by the method and the experimental observations verifies the accuracy and applicability of the method.

Keywords: ice contour boundary, image processing, image segmentation, salt ice, thermal image

Procedia PDF Downloads 319

Authors: Sawitree Suvanno

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The objective of this study is to investigate the Suan Sunandha Rajabhat University (SSRU) image among people in Bangkok. This study was conducted in the quantitative research and the questionnaires were used to collect data from 360 people of a sample group. Descriptive and inferential statistics were used in data analysis. The result showed that the SSRU’s image among people in Bangkok is in the “rather true” level of questionnaire scale in all aspects measured. The aspect that gains the utmost average is that the university is considered as royal-oriented and conservative; 2) the instructional supplies, buildings and venue promoting Thai art and tradition; 3) the moral and honest university administration; 4) the curriculum and the skillful students as well as graduates. Additional, people in Bangkok with different profession have the different view to the SSRU’s image at the significant level 0.05; there is no significant difference in gender, age and income.

Keywords: Bangkok, demographics, image, Suan Sunandha Rajabhpat University

Procedia PDF Downloads 245

Authors: Daira Radouane, Naim Boudmagh, Hamada Adel

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The but of this handling is to use the technique of the shearing with a mechanism lapping machine of image: a prism of Wollaston. We want to characterize this prism in order to be able to employ it later on in an analysis by shearing. A prism of Wollaston is a prism produced in a birefringent material i.e. having two indexes of refraction. This prism is cleaved so as to present the directions associated with these indices in its face with entry. It should be noted that these directions are perpendicular between them.

Keywords: non destructive control, aluminium, interferometry, treatment of image

Procedia PDF Downloads 326

Authors: N. W. U. D. Chathurani, Shlomo Geva, Vinod Chandran, Proboda Rajapaksha

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Selecting an appropriate image representation is the most important factor in implementing an effective Content-Based Image Retrieval (CBIR) system. This paper presents a multi-feature fusion approach for efficient CBIR, based on the distance distribution of features and relative feature weights at the time of query processing. It is a simple yet effective approach, which is free from the effect of features' dimensions, ranges, internal feature normalization and the distance measure. This approach can easily be adopted in any feature combination to improve retrieval quality. The proposed approach is empirically evaluated using two benchmark datasets for image classification (a subset of the Corel dataset and Oliva and Torralba) and compared with existing approaches. The performance of the proposed approach is confirmed with the significantly improved performance in comparison with the independently evaluated baseline of the previously proposed feature fusion approaches.

Keywords: feature fusion, image retrieval, membership function, normalization

Procedia PDF Downloads 344

Authors: Md. Minhazul Islam, Shah Ashisul Abed Nipun, Majharul Islam, Md. Abdur Rakib Rahat, Jonayet Miah, Salsavil Kayyum, Anwar Shadaab, Faiz Al Faisal

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The target of this research is to predict Myocardial Infarction using unsupervised Machine Learning algorithms. Myocardial Infarction Prediction related to heart disease is a challenging factor faced by doctors & hospitals. In this prediction, accuracy of the heart disease plays a vital role. From this concern, the authors have analyzed on a myocardial dataset to predict myocardial infarction using some popular Machine Learning algorithms K-Means and Hierarchical Clustering. This research includes a collection of data and the classification of data using Machine Learning Algorithms. The authors collected 345 instances along with 26 attributes from different hospitals in Bangladesh. This data have been collected from patients suffering from myocardial infarction along with other symptoms. This model would be able to find and mine hidden facts from historical Myocardial Infarction cases. The aim of this study is to analyze the accuracy level to predict Myocardial Infarction by using Machine Learning techniques.

Keywords: Machine Learning, K-means, Hierarchical Clustering, Myocardial Infarction, Heart Disease

Procedia PDF Downloads 201

Authors: Chiao-Chen Chang, Yang-Chieh Chin

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The mobile phone has no longer confined to communication, from the aspect of smartphones, consumers are only willing to pay for the product which the added value has corresponded with their appetites, such as multiple application, upgrade of the camera, and the appearance of the phone and so on. Moreover, as the maturity stage of smartphone industry today, the strategy which manufactures used to gain competitive advantages through hardware as well as software differentiation, is no longer valid. Thus, this research aims to initiate from brand image, to examine exactly whether consumers’ buying intention focus on smartphone brand or operating system, at the same time, perceived value and customer satisfaction will be added between brand image and continuous usage intention to investigate the impact of these two facets toward continuous usage intention. This study verifies the correlation, fitness, and relationship between the variables that lies within the conceptual framework. The result of using structural equation modeling shows that brand image has a positive impact on continuous usage intention. Firms can affect consumer perceived value and customer satisfaction through the creation of the brand image. It also shows that the brand image of smartphone and brand image of the operating system have a positive impact on customer perceived value and customer satisfaction. Furthermore, perceived value also has a positive impact on satisfaction, and so is the relation within satisfaction and perceived value to the continuous usage intention. Last but not least, the brand image of the smartphone has a more remarkable impact on customers than the brand image of the operating system. In addition, this study extends the results to management practice and suggests manufactures to provide fine product design and hardware.

Keywords: smartphone, brand image, perceived value, continuous usage intention

Procedia PDF Downloads 196

Authors: Pengchao Wei, Xiangzhong Fang

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Noise is ubiquitous in this world. Denoising is an essential technology, especially in image semantic segmentation, where noises are generally categorized into two main types i.e. feature noise and label noise. The main focus of this paper is aiming at modeling label noise, investigating the behaviors of different types of label noise on image semantic segmentation tasks using K-Nearest-Neighbor and Convolutional Neural Network classifier. The performance without label noise and with is evaluated and illustrated in this paper. In addition to that, the influence of feature noise on the image semantic segmentation task is researched as well and a feature noise reduction method is applied to mitigate its influence in the learning procedure.

Keywords: convolutional neural network, denoising, feature noise, image semantic segmentation, k-nearest-neighbor, label noise

Procedia PDF Downloads 218

Authors: Ahd Aljarf, Saad Amin

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Images are important source of information used as evidence during any investigation process. Their clarity and accuracy is essential and of the utmost importance for any investigation. Images are vulnerable to losing blocks and having noise added to them either after alteration or when the image was taken initially, therefore, having a high performance image processing system and it is implementation is very important in a forensic point of view. This paper focuses on improving the quality of the forensic images. For different reasons packets that store data can be affected, harmed or even lost because of noise. For example, sending the image through a wireless channel can cause loss of bits. These types of errors might give difficulties generally for the visual display quality of the forensic images. Two of the images problems: noise and losing blocks are covered. However, information which gets transmitted through any way of communication may suffer alteration from its original state or even lose important data due to the channel noise. Therefore, a developed system is introduced to improve the quality and clarity of the forensic images.

Keywords: image filtering, image reconstruction, image processing, forensic images

Procedia PDF Downloads 361

Authors: Justie Su-Tzu Juan, Ming-Jheng Li, Ching-Fen Lee, Ruei-Yu Wu

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A secret image sharing scheme is a way to protect images. The main idea is dispersing the secret image into numerous shadow images. A secret image sharing scheme can withstand the impersonal attack and achieve the highly practical property of multiuse  is more practical. Therefore, this paper proposes a verifiable and detectable secret image-sharing scheme called VDGMSISS to solve the impersonal attack and to achieve some properties such as encrypting multi-secret images at one time and multi-use. Moreover, our scheme can also be used for any genera access structure.

Keywords: multi-secret image sharing scheme, verifiable, de-tectable, general access structure

Procedia PDF Downloads 123

Authors: Komal V. Aher, Sanjay B. Waykar

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Nowadays every individual having mobile device with them. In both computer vision and information retrieval Image search is currently hot topic with many applications. The proposed intelligent image search system is fully utilizing multimodal and multi-touch functionalities of smart phones which allows search with Image, Voice, and Text on mobile phones. The system will be more useful for users who already have pictures in their minds but have no proper descriptions or names to address them. The paper gives system with ability to form composite visual query to express user’s intention more clearly which helps to give more precise or appropriate results to user. The proposed algorithm will considerably get better in different aspects. System also uses Context based Image retrieval scheme to give significant outcomes. So system is able to achieve gain in terms of search performance, accuracy and user satisfaction.

Keywords: color space, histogram, mobile device, mobile visual search, multimodal search

Procedia PDF Downloads 366

Authors: Emiel Caron

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Patent databases contain patent related data, organized in a relational data model, and are used to produce various patent statistics. These databases store raw data about scientific references cited by patents. For example, Patstat holds references to tens of millions of scientific journal publications and conference proceedings. These references might be used to connect patent databases with bibliographic databases, e.g. to study to the relation between science, technology, and innovation in various domains. Problematic in such studies is the low data quality of the references, i.e. they are often ambiguous, unstructured, and incomplete. Moreover, a complete bibliographic reference is stored in only one attribute. Therefore, a computerized cleaning and disambiguation method for large patent databases is developed in this work. The method uses rule-based scoring and clustering. The rules are based on bibliographic metadata, retrieved from the raw data by regular expressions, and are transparent and adaptable. The rules in combination with string similarity measures are used to detect pairs of records that are potential duplicates. Due to the scoring, different rules can be combined, to join scientific references, i.e. the rules reinforce each other. The scores are based on expert knowledge and initial method evaluation. After the scoring, pairs of scientific references that are above a certain threshold, are clustered by means of single-linkage clustering algorithm to form connected components. The method is designed to disambiguate all the scientific references in the Patstat database. The performance evaluation of the clustering method, on a large golden set with highly cited papers, shows on average a 99% precision and a 95% recall. The method is therefore accurate but careful, i.e. it weighs precision over recall. Consequently, separate clusters of high precision are sometimes formed, when there is not enough evidence for connecting scientific references, e.g. in the case of missing year and journal information for a reference. The clusters produced by the method can be used to directly link the Patstat database with bibliographic databases as the Web of Science or Scopus.

Keywords: clustering, data cleaning, data disambiguation, data mining, patent analysis, scientometrics

Procedia PDF Downloads 192

Authors: Jiankang Wang, Hongyang Yu

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This paper proposes a portable online 3D modeling method. The method first utilizes a depth camera to collect data and compresses the depth data using a frame-by-frame lossless data compression method. The color image is encoded using the H.264 encoding format. After the cloud obtains the color image and depth image, a 3D modeling method based on bundlefusion is used to complete the 3D modeling. The results of this study indicate that this method has the characteristics of portability, online, and high efficiency and has a wide range of application prospects.

Keywords: 3D reconstruction, bundlefusion, lossless compression, depth image

Procedia PDF Downloads 80

Authors: Mahasa Zahirnia

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The purpose of this paper is to explore new and effective ways of optimizing satellite images using artificial intelligence, and the process of implementing reinforcement learning to enhance the quality of data captured within the image. In our implementation of Bellman's Reinforcement Learning equations, associated state diagrams, and multi-stage image processing, we were able to enhance image quality, detect and define objects. Reinforcement learning is the differentiator in the area of artificial intelligence, and Q-Learning relies on trial and error to achieve its goals. The reward system that is embedded in Q-Learning allows the agent to self-evaluate its performance and decide on the best possible course of action based on the current and future environment. Results show that within a simulated environment, built on the images that are commercially available, the rate of detection was 40-90%. Reinforcement learning through Q-Learning algorithm is not just desired but required design criteria for image optimization and enhancements. The proposed methods presented are a cost effective method of resolving uncertainty of the data because reinforcement learning finds ideal policies to manage the process using a smaller sample of images.

Keywords: Q-learning, image optimization, reinforcement learning, Markov decision process

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Authors: Cherifi Mehdi, Lahdir Mourad, Ameur Soltane

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Image compression is used to reduce the number of bits required to represent an image. The Meteosat Second Generation satellite (MSG) allows the acquisition of 12 image files every 15 minutes. Which results a large databases sizes. The transform selected in the images compression should contribute to reduce the data representing the images. The Radon transform retrieves the Radon points that represent the sum of the pixels in a given angle for each direction. Linear predictive coding (LPC) with filtering provides a good decorrelation of Radon points using a Predictor constitute by the Symmetric Nearest Neighbor filter (SNN) coefficients, which result losses during decompression. Finally, Run Length Coding (RLC) gives us a high and fixed compression ratio regardless of the input image. In this paper, a novel image compression method based on the Radon transform and linear predictive coding (LPC) for MSG images is proposed. MSG image compression based on the Radon transform and the LPC provides a good compromise between compression and quality of reconstruction. A comparison of our method with other whose two based on DCT and one on DWT bi-orthogonal filtering is evaluated to show the power of the Radon transform in its resistibility against the quantization noise and to evaluate the performance of our method. Evaluation criteria like PSNR and the compression ratio allows showing the efficiency of our method of compression.

Keywords: image compression, radon transform, linear predictive coding (LPC), run lengthcoding (RLC), meteosat second generation (MSG)

Procedia PDF Downloads 418

Authors: Ramandeep Kaur, Kamaljit Kaur

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In recent years, the technology of the remote sensing is growing rapidly. Image enhancement is one of most commonly used of image processing operations. Noise reduction plays very important role in digital image processing and various technologies have been located ahead to reduce the noise of the remote sensing images. The noise reduction using wavelet coefficients based on Orthogonal Matching Pursuit (OMP) has less consequences on the edges than available methods but this is not as establish in edge preservation techniques. So in this paper we provide a new technique minimum patch based noise reduction OMP which reduce the noise from an image and used edge preservation patch which preserve the edges of the image and presents the superior results than existing OMP technique. Experimental results show that the proposed minimum patch approach outperforms over existing techniques.

Keywords: image denoising, minimum patch, OMP, WCOMP

Procedia PDF Downloads 386

Authors: Shreya Pare, Parvin Akhter

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Remotely sensing image classification becomes a very challenging task owing to the high dimensionality of hyperspectral images. The pixel-wise classification methods fail to take the spatial structure information of an image. Therefore, to improve the performance of classification, spatial information can be integrated into the classification process. In this paper, the multilevel thresholding algorithm based on a modified fuzzy entropy function is used to perform the segmentation of hyperspectral images. The fuzzy parameters of the MFE function have been optimized by using a new meta-heuristic algorithm based on the Tree-Search algorithm. The segmented image is classified by a large distribution machine (LDM) classifier. Experimental results are shown on a hyperspectral image dataset. The experimental outputs indicate that the proposed technique (MFE-TSA-LDM) achieves much higher classification accuracy for hyperspectral images when compared to state-of-art classification techniques. The proposed algorithm provides accurate segmentation and classification maps, thus becoming more suitable for image classification with large spatial structures.

Keywords: classification, hyperspectral images, large distribution margin, modified fuzzy entropy function, multilevel thresholding, tree search algorithm, hyperspectral image classification using tree search algorithm

Procedia PDF Downloads 175

Authors: Areej Salaymeh, Loren Schwiebert, Stephen Remias, Jonathan Waddell

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Extracting information from new data sources has emerged as a crucial task in many traffic planning processes, such as identifying traffic patterns, route planning, traffic forecasting, and locating infrastructure improvements. Given the advanced technologies used to collect Global Positioning System (GPS) data from dedicated GPS devices, GPS equipped phones, and navigation tools, intelligent data analysis methodologies are necessary to mine this raw data. In this research, an automatic detection framework is proposed to help identify and classify the locations of stopped GPS waypoints into two main categories: signalized intersections or highway congestion. The Delaunay triangulation is used to perform this assessment in the clustering phase. While most of the existing clustering algorithms need assumptions about the data distribution, the effectiveness of the Delaunay triangulation relies on triangulating geographical data points without such assumptions. Our proposed method starts by cleaning noise from the data and normalizing it. Next, the framework will identify stoppage points by calculating the traveled distance. The last step is to use clustering to form groups of waypoints for signalized traffic and highway congestion. Next, a binary classifier was applied to find distinguish highway congestion from signalized stop points. The binary classifier uses the length of the cluster to find congestion. The proposed framework shows high accuracy for identifying the stop positions and congestion points in around 99.2% of trials. We show that it is possible, using limited GPS data, to distinguish with high accuracy.

Keywords: Delaunay triangulation, clustering, intelligent transportation systems, GPS data

Procedia PDF Downloads 273

Authors: Azam Fazelpour, Saeed R. Dehghani, Vlastimil Masek, Yuri S. Muzychka

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This study employs a method based on image analyses and structure information to detect accumulated ice on known structures. The icing of marine vessels and offshore structures causes significant reductions in their efficiency and creates unsafe working conditions. Image processing methods are used to measure ice loads automatically. Most image processing methods are developed based on captured image analyses. In this method, ice loads on structures are calculated by defining structure coordinates and processing captured images. A pyramidal structure is designed with nine cylindrical bars as the known structure of experimental setup. Unsymmetrical ice accumulated on the structure in a cold room represents the actual case of experiments. Camera intrinsic and extrinsic parameters are used to define structure coordinates in the image coordinate system according to the camera location and angle. The thresholding method is applied to capture images and detect iced structures in a binary image. The ice thickness of each element is calculated by combining the information from the binary image and the structure coordinate. Averaging ice diameters from different camera views obtains ice thicknesses of structure elements. Comparison between ice load measurements using this method and the actual ice loads shows positive correlations with an acceptable range of error. The method can be applied to complex structures defining structure and camera coordinates.

Keywords: camera calibration, ice detection, ice load measurements, image processing

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Authors: Aftab Khan, Ashfaq Khan

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The research paper focuses on an interesting challenge faced in Blind Image Deblurring (BID). It relates to the estimation of arbitrarily shaped or non-parametric Point Spread Functions (PSFs) of motion blur caused by camera handshake. These PSFs exhibit much more complex shapes than their parametric counterparts and deblurring in this case requires intricate ways to estimate the blur and effectively remove it. This research work introduces a novel blind deblurring scheme visualized for deblurring images corrupted by arbitrarily shaped PSFs. It is based on Genetic Algorithm (GA) and utilises the Blind/Reference-less Image Spatial QUality Evaluator (BRISQUE) measure as the fitness function for arbitrarily shaped PSF estimation. The proposed BID scheme has been compared with other single image motion deblurring schemes as benchmark. Validation has been carried out on various blurred images. Results of both benchmark and real images are presented. Non-reference image quality measures were used to quantify the deblurring results. For benchmark images, the proposed BID scheme using BRISQUE converges in close vicinity of the original blurring functions.

Keywords: blind deconvolution, blind image deblurring, genetic algorithm, image restoration, image quality measures

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Authors: Jianfeng Hu

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Personal authentication based on electroencephalography (EEG) signals is one of the important field for the biometric technology. More and more researchers have used EEG signals as data source for biometric. However, there are some disadvantages for biometrics based on EEG signals. The proposed method employs entropy measures for feature extraction from EEG signals. Four type of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE) and spectral entropy (PE), were deployed as feature set. In a silhouettes calculation, the distance from each data point in a cluster to all another point within the same cluster and to all other data points in the closest cluster are determined. Thus silhouettes provide a measure of how well a data point was classified when it was assigned to a cluster and the separation between them. This feature renders silhouettes potentially well suited for assessing cluster quality in personal authentication methods. In this study, “silhouettes scores” was used for assessing the cluster quality of k-means clustering algorithm is well suited for comparing the performance of each EEG dataset. The main goals of this study are: (1) to represent each target as a tuple of multiple feature sets, (2) to assign a suitable measure to each feature set, (3) to combine different feature sets, (4) to determine the optimal feature weighting. Using precision/recall evaluations, the effectiveness of feature weighting in clustering was analyzed. EEG data from 22 subjects were collected. Results showed that: (1) It is possible to use fewer electrodes (3-4) for personal authentication. (2) There was the difference between each electrode for personal authentication (p<0.01). (3) There is no significant difference for authentication performance among feature sets (except feature PE). Conclusion: The combination of k-means clustering algorithm and silhouette approach proved to be an accurate method for personal authentication based on EEG signals.

Keywords: personal authentication, K-mean clustering, electroencephalogram, EEG, silhouettes

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Authors: Mehtab Alam, Mudiarasan Kuppusamy

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Image branding is the prominent and well-guided phenomena of managing tourism destinations. It examines the image of cities forming as brand identity. Transformation of cities into tourist destinations is obligatory for the current management practices to be used for foreign policy. The research considers the features of perception, destination accommodation, destination quality, traveler revisit, destination information system, and behavioral image for tourism destination management. Using the quantitative and qualitative research methodology, the objective is to examine and investigate the opportunities for destination branding. It investigates the features and management of tourism destinations in Abbottabad city of Pakistan through SPSS and NVivo 12 software. The prospective outlook of the results and coding reflects the significant contribution of integrated destination management for image branding, where Abbottabad has the potential to become a destination city. The positive impact of branding integrates tourism management as it is fulfilling travelers’ requirements to influence the choice of destination for innovative foreign policy.

Keywords: image branding, destination management, tourism, foreign policy, innovative

Procedia PDF Downloads 90

Authors: Mohammad Rostami, Mohammad Reza Forghani, Elahe Neshat, Fatemeh Yaghoobi

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An Ad Hoc network consists of wireless mobile equipment which connects to each other without any infrastructure, using connection equipment. The best way to form a hierarchical structure is clustering. Various methods of clustering can form more stable clusters according to nodes' mobility. In this research we propose an algorithm, which allocates some weight to nodes based on factors, i.e. link stability and power reduction rate. According to the allocated weight in the previous phase, the cellular learning automaton picks out in the second phase nodes which are candidates for being cluster head. In the third phase, learning automaton selects cluster head nodes, member nodes and forms the cluster. Thus, this automaton does the learning from the setting and can form optimized clusters in terms of power consumption and link stability. To simulate the proposed algorithm we have used omnet++4.2.2. Simulation results indicate that newly formed clusters have a longer lifetime than previous algorithms and decrease strongly network overload by reducing update rate.

Keywords: mobile Ad Hoc networks, clustering, learning automaton, cellular automaton, battery power

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Authors: Adnan H. M. Al-Helali, Hamza A. Ali

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Growth of censorship and pervasive monitoring on the Internet, Steganography arises as a new means of achieving secret communication. Steganography is the art and science of embedding information within electronic media used by common applications and systems. Generally, hiding information of multimedia within images will change some of their properties that may introduce few degradation or unusual characteristics. This paper presents a new image steganography approach for hiding information of multimedia (images, text, and audio) using generated Mandelbrot Fractal image as a cover. The proposed technique has been extensively tested with different images. The results show that the method is a very secure means of hiding and retrieving steganographic information. Experimental results demonstrate that an effective improvement in the values of the Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Normalized Cross Correlation (NCC) and Image Fidelity (IF) over the previous techniques.

Keywords: fractal image, information hiding, Mandelbrot et fractal, steganography

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Authors: Yakup Uslu

Abstract:

The purpose of this study is to reveal how the Turkish soap operas effect the image of Turkey in the Middle Europe. In last decades, Turkish soap operas have been shown on TV in the middle European countries. A research based on face to face questioning was done in February and June 2014 in Slovakia and the Czech Republic. The participants were seven women and six men from the Czech Republic, 8 women and 6 men from Slovakia. According to results of the research, the Turkish image has been changed substantially after broadcasting the soap operas. In general, the Turkish soap operas have had positive effects on the image of Turkey. The other result of the study shows that most of the people in Slovakia and Czech Republic want to come to Turkey as tourists and want to visit the places where the soap operas have been shooted.

Keywords: Turkish soap operas, image of Turkey, Slovakia, Czech Republic

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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With the continuous increment of smart meter installations across the globe, the need for processing of the load data is evident. Clustering-based load profiling is built upon the utilization of unsupervised machine learning tools for the purpose of formulating the typical load curves or load profiles. The most commonly used algorithm in the load profiling literature is the K-means. While the algorithm has been successfully tested in a variety of applications, its drawback is the strong dependence in the initialization phase. This paper proposes a novel modified form of the K-means that addresses the aforementioned problem. Simulation results indicate the superiority of the proposed algorithm compared to the K-means.

Keywords: clustering, load profiling, load modeling, machine learning, energy efficiency and quality

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Authors: Adnan H. M. Al-Helali, Hamza A. Ali

Abstract:

The growth of censorship and pervasive monitoring on the Internet, Steganography arises as a new means of achieving secret communication. Steganography is the art and science of embedding information within electronic media used by common applications and systems. Generally, hiding information of multimedia within images will change some of their properties that may introduce few degradation or unusual characteristics. This paper presents a new image steganography approach for hiding information of multimedia (images, text, and audio) using generated Mandelbrot Fractal image as a cover. The proposed technique has been extensively tested with different images. The results show that the method is a very secure means of hiding and retrieving steganographic information. Experimental results demonstrate that an effective improvement in the values of the Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Normalized Cross Correlation (NCC), and Image Fidelity (IF) over the pervious techniques.

Keywords: fractal image, information hiding, Mandelbrot set fractal, steganography

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Authors: Aticha Kwaengsopha, Kevin Wongleedee

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Destination image can cause an increase and decrease in the growth rate of international tourists visiting Thailand. This paper drew upon data collected from an international tourist survey conducted in Bangkok, Thailand during January to March of 2014. The survey was structured primarily to obtain international tourists’ opinions towards the importance of destination image factors that they encountered during their trip in Thailand. A total of 200 respondents were elicited as data input for mean, SD, and t-test. The findings revealed that the overall level of importance of these factors was not very high. The findings also revealed the three most important factors as tourist experience, interesting place, and pleasing destination. In addition, the result for t-test revealed that there was not much effect from gender differences in opinions of the level concerning importance for destination image factors.

Keywords: destination image, international tourists, Thailand, revisit

Procedia PDF Downloads 334