Search results for: Image Mining

Authors: J. I. Asnor, S. Rosnah, Z. W. H. Wan, H. A. B. Badrul

Abstract:

Pineapples can be classified using an index with seven levels of maturity based on the green and yellow color of the skin. As the pineapple ripens, the skin will change from pale green to a golden or yellowish color. The issues that occur in agriculture nowadays are to do with farmers being unable to distinguish between the indexes of pineapple maturity correctly and effectively. There are several reasons for why farmers cannot properly follow the guideline provide by Federal Agriculture Marketing Authority (FAMA) and one of reason is that due to manual inspection done by experts, there are no specific and universal guidelines to be adopted by farmers due to the different points of view of the experts when sorting the pineapples based on their knowledge and experience. Therefore, an automatic system will help farmers to identify pineapple maturity effectively and will become a universal indicator to farmers.

Keywords: Artificial Neural Network, Image Processing, Index of Maturity, Pineapple

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Authors: Panos D. Kotsas

Abstract:

This paper presents the application of a signal intensity independent registration criterion for 2D rigid body registration of medical images using 1D binary projections. The criterion is defined as the weighted ratio of two projections. The ratio is computed on a pixel per pixel basis and weighting is performed by setting the ratios between one and zero pixels to a standard high value. The mean squared value of the weighted ratio is computed over the union of the one areas of the two projections and it is minimized using the Chebyshev polynomial approximation using n=5 points. The sum of x and y projections is used for translational adjustment and a 45deg projection for rotational adjustment. 20 T1- T2 registration experiments were performed and gave mean errors 1.19deg and 1.78 pixels. The method is suitable for contour/surface matching. Further research is necessary to determine the robustness of the method with regards to threshold, shape and missing data.

Keywords: Medical image, projections, registration, rigid.

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Authors: YasirSalih, AamirSaeed Malik, Wazirahbt.Md-Esa

Abstract:

In the recent years, high dynamic range imaging has gain popularity with the advancement in digital photography. In this contribution we present a subjective evaluation of various tone production and tone mapping techniques by a number of participants. Firstly, standard HDR images were used and the participants were asked to rate them based on a given rating scheme. After that, the participant was asked to rate HDR image generated using linear and nonlinear combination approach of multiple exposure images. The experimental results showed that linearly generated HDR images have better visualization than the nonlinear combined ones. In addition, Reinhard et al. and the exponential tone mapping operators have shown better results compared to logarithmic and the Garrett et al. tone mapping operators.

Keywords: tone mapping, high dynamic range, low dynamic range, bits per pixel.

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Authors: John N. Ellinas

Abstract:

In this paper, a robust watermarking algorithm using the wavelet transform and edge detection is presented. The efficiency of an image watermarking technique depends on the preservation of visually significant information. This is attained by embedding the watermark transparently with the maximum possible strength. The watermark embedding process is carried over the subband coefficients that lie on edges, where distortions are less noticeable, with a subband level dependent strength. Also, the watermark is embedded to selected coefficients around edges, using a different scale factor for watermark strength, that are captured by a morphological dilation operation. The experimental evaluation of the proposed method shows very good results in terms of robustness and transparency to various attacks such as median filtering, Gaussian noise, JPEG compression and geometrical transformations.

Keywords: Watermarking, wavelet transform, edge detection.

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Authors: Walter Hussak, Heiko Schröder

Abstract:

Star graphs are Cayley graphs of symmetric groups of permutations, with transpositions as the generating sets. A star graph is a preferred interconnection network topology to a hypercube for its ability to connect a greater number of nodes with lower degree. However, an attractive property of the hypercube is that it has a Hamiltonian decomposition, i.e. its edges can be partitioned into disjoint Hamiltonian cycles, and therefore a simple routing can be found in the case of an edge failure. The existence of Hamiltonian cycles in Cayley graphs has been known for some time. So far, there are no published results on the much stronger condition of the existence of Hamiltonian decompositions. In this paper, we give a construction of a Hamiltonian decomposition of the star graph 5-star of degree 4, by defining an automorphism for 5-star and a Hamiltonian cycle which is edge-disjoint with its image under the automorphism.

Keywords: interconnection networks, paths and cycles, graphs andgroups.

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Authors: Gameel Abdelmageed

Abstract:

Arabs have a rhetorical heritage which has greatly contributed to the monitoring and analyzing of the rhetoric of the Holy Quran, Hadith, and Arabic texts on poetry and oratory. But Arab scholars - as far as the researcher knows – have not contributed to monitoring and analyzing the rhetoric of digital Arabic discourse although it has prominence, particularly in social media and has strong effectiveness in the political and social life of Arab society. This discourse has made its impact by using very new rhetorical techniques in language, voice, image, painting and video clips which are known as “Multimedia” and belong to “Digital Rhetoric”. This study suggests that it is time to draw the attention of Arab scholars and invite them to monitor and analyze the rhetoric of digital Arabic discourse.

Keywords: Digital discourse, digital rhetoric, social media, Facebook.

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Authors: Tlotlollo Sidwell Hlalele, Shengzhi Du

Abstract:

High-voltage power transmission lines are the back bone of electrical power utilities. The stability and continuous monitoring of this critical infrastructure is pivotal. Nine-Sigma representing Eskom Holding SOC limited, South Africa has a major problem on proactive detection of fallen power lines and real time sagging measurement together with slipping of such conductors. The main objective of this research is to innovate RFID technology to solve this challenge. Various options and technologies such as GPS, PLC, image processing, MR sensors and etc., have been reviewed and draw backs were made. The potential of RFID to give precision measurement will be observed and presented. The future research will look at magnetic and electrical interference as well as corona effect on the technology.

Keywords: Precision Measurement, RFID and Sag.

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Authors: Zhuang He, Andrei Kotousov

Abstract:

All current experimental methods for determination of stress intensity factors are based on the assumption that the state of stress near the crack tip is plane stress. Therefore, these methods rely on strain and displacement measurements made outside the near crack tip region affected by the three-dimensional effects or by process zone. In this paper, we develop and validate an experimental procedure for the evaluation of stress intensity factors from the measurements of the out-of-plane displacements in the surface area controlled by 3D effects. The evaluation of stress intensity factors is possible when the process zone is sufficiently small, and the displacement field generated by the 3D effects is fully encapsulated by K-dominance region.

Keywords: Digital image correlation, stress intensity factors, three-dimensional effects, transverse displacement.

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Authors: Loai Nasrat, Mervat S. Hassan

Abstract:

Bentonitic material from South Aswan, Egypt was evaluated in terms of mineral-ogy and chemical composition as bleaching clay in refining of transformer oil before and after acid activation and thermal treatment followed by acid leaching using HCl and H2SO4 for different contact times. Structural modification and refining power of bento-nite were investigated during modification by means of X-ray diffraction and infrared spectroscopy. The results revealed that the activated bentonite could be used for refining of transformer oil. The oil parameters such as; dielectric strength, viscosity and flash point had been improved. The dielectric breakdown strength of used oil increased from 29 kV for used oil treated with unactivated bentonite to 74 kV after treatment with activated bentonite. Kinematic Viscosity changed from 19 to 11 mm2 /s after treatment with activated bentonite. However, flash point achieved 149 ºC.

Keywords: Dielectric strength, unactivated bentonite, X-ray diffraction, SEM image

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Authors: Yasuomi D. Sato, Yasutaka Kuriya

Abstract:

We here propose improved version of elastic graph matching (EGM) as a face detector, called the multi-scale EGM (MS-EGM). In this improvement, Gabor wavelet-based pyramid reduces computational complexity for the feature representation often used in the conventional EGM, but preserving a critical amount of information about an image. The MS-EGM gives us higher detection performance than Viola-Jones object detection algorithm of the AdaBoost Haar-like feature cascade. We also show rapid detection speeds of the MS-EGM, comparable to the Viola-Jones method. We find fruitful benefits in the MS-EGM, in terms of topological feature representation for a face.

Keywords: Face detection, Gabor wavelet based pyramid, elastic graph matching, topological preservation, redundancy of computational complexity.

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Authors: Watcharin Kaewapichai, Pakorn Kaewtrakulpong

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In this paper, a method to detect multiple ellipses is presented. The technique is efficient and robust against incomplete ellipses due to partial occlusion, noise or missing edges and outliers. It is an iterative technique that finds and removes the best ellipse until no reasonable ellipse is found. At each run, the best ellipse is extracted from randomly selected edge patches, its fitness calculated and compared to a fitness threshold. RANSAC algorithm is applied as a sampling process together with the Direct Least Square fitting of ellipses (DLS) as the fitting algorithm. In our experiment, the method performs very well and is robust against noise and spurious edges on both synthetic and real-world image data.

Keywords: Direct Least Square Fitting, Ellipse Detection, RANSAC

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Authors: Rodrigo Montufar-Chaveznava

Abstract:

The colors of the human skin represent a special category of colors, because they are distinctive from the colors of other natural objects. This category is found as a cluster in color spaces, and the skin color variations between people are mostly due to differences in the intensity. Besides, the face detection based on skin color detection is a faster method as compared to other techniques. In this work, we present a system to track faces by carrying out skin color detection in four different color spaces: HSI, YCbCr, YES and RGB. Once some skin color regions have been detected for each color space, we label each and get some characteristics such as size and position. We are supposing that a face is located in one the detected regions. Next, we compare and employ a polling strategy between labeled regions to determine the final region where the face effectively has been detected and located.

Keywords: Tracking, face detection, image processing, colorspaces.

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Authors: Jang-Hee Yoo, Doosung Hwang, Jong-Wook Han, Ki-Young Moon

Abstract:

The ability to recognize humans and their activities by computer vision is a very important task, with many potential application. Study of human motion analysis is related to several research areas of computer vision such as the motion capture, detection, tracking and segmentation of people. In this paper, we describe a segmentation method for extracting human body contour in modified HLS color space. To estimate a background, the modified HLS color space is proposed, and the background features are estimated by using the HLS color components. Here, the large amount of human dataset, which was collected from DV cameras, is pre-processed. The human body and its contour is successfully extracted from the image sequences.

Keywords: Background Subtraction, Human Silhouette Extraction, HLS Color Space, and Object Segmentation

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Authors: Emel Kızılkaya Aydogan, Mihrimah Ozmen, Yılmaz Delice

Abstract:

In recent days, there is a change and the ongoing development of the telecommunications sector in the global market. In this sector, churn analysis techniques are commonly used for analysing why some customers terminate their service subscriptions prematurely. In addition, customer churn is utmost significant in this sector since it causes to important business loss. Many companies make various researches in order to prevent losses while increasing customer loyalty. Although a large quantity of accumulated data is available in this sector, their usefulness is limited by data quality and relevance. In this paper, a cost-sensitive feature selection framework is developed aiming to obtain the feature reducts to predict customer churn. The framework is a cost based optional pre-processing stage to remove redundant features for churn management. In addition, this cost-based feature selection algorithm is applied in a telecommunication company in Turkey and the results obtained with this algorithm.

Keywords: Churn prediction, data mining, decision-theoretic rough set, feature selection.

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Authors: K.Manimala, Dr.K.Selvi, R.Ahila

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Artificial Intelligence (AI) methods are increasingly being used for problem solving. This paper concerns using AI-type learning machines for power quality problem, which is a problem of general interest to power system to provide quality power to all appliances. Electrical power of good quality is essential for proper operation of electronic equipments such as computers and PLCs. Malfunction of such equipment may lead to loss of production or disruption of critical services resulting in huge financial and other losses. It is therefore necessary that critical loads be supplied with electricity of acceptable quality. Recognition of the presence of any disturbance and classifying any existing disturbance into a particular type is the first step in combating the problem. In this work two classes of AI methods for Power quality data mining are studied: Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). We show that SVMs are superior to ANNs in two critical respects: SVMs train and run an order of magnitude faster; and SVMs give higher classification accuracy.

Keywords: back propagation network, power quality, probabilistic neural network, radial basis function support vector machine

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Authors: B. Lorestani, M. Cheraghi, N. Yousefi

Abstract:

A research project dealing with the phytoremediation of a soil polluted by some heavy metals is currently running. The case study is represented by a mining area in Hamedan province in the central west part of Iran. The potential of phytoextraction and phytostabilization of plants was evaluated considering the concentration of heavy metals in the plant tissues and also the bioconcentration factor (BCF) and the translocation factor (TF). Also the several established criteria were applied to define hyperaccumulator plants in the studied area. Results showed that none of the collected plant species were suitable for phytoextraction of Cu, Zn, Fe and Mn, but among the plants, Euphorbia macroclada was the most efficient in phytostabilization of Cu and Fe, while, Ziziphora clinopodioides, Cousinia sp. and Chenopodium botrys were the most suitable for phytostabilization of Zn and Chondrila juncea and Stipa barbata had the potential for phytostabilization of Mn. Using the most common criterion, Euphorbia macroclada and Verbascum speciosum were Fe hyperaccumulator plants. Present study showed that native plant species growing on contaminated sites may have the potential for phytoremediation.

Keywords: Bioconcentration factor, Heavy metals, Hyperaccumulator, Phytoremediation, Translocation factor

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Authors: Hanan Ahmed-Hosni Mahmoud, Nadia Al-Ghreimil

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In-place sorting algorithms play an important role in many fields such as very large database systems, data warehouses, data mining, etc. Such algorithms maximize the size of data that can be processed in main memory without input/output operations. In this paper, a novel in-place sorting algorithm is presented. The algorithm comprises two phases; rearranging the input unsorted array in place, resulting segments that are ordered relative to each other but whose elements are yet to be sorted. The first phase requires linear time, while, in the second phase, elements of each segment are sorted inplace in the order of z log (z), where z is the size of the segment, and O(1) auxiliary storage. The algorithm performs, in the worst case, for an array of size n, an O(n log z) element comparisons and O(n log z) element moves. Further, no auxiliary arithmetic operations with indices are required. Besides these theoretical achievements of this algorithm, it is of practical interest, because of its simplicity. Experimental results also show that it outperforms other in-place sorting algorithms. Finally, the analysis of time and space complexity, and required number of moves are presented, along with the auxiliary storage requirements of the proposed algorithm.

Keywords: Auxiliary storage sorting, in-place sorting, sorting.

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Authors: Fatima Akhmedova, Simon Liao

Abstract:

One of the most critical decision points in the design of a face recognition system is the choice of an appropriate face representation. Effective feature descriptors are expected to convey sufficient, invariant and non-redundant facial information. In this work we propose a set of Hahn moments as a new approach for feature description. Hahn moments have been widely used in image analysis due to their invariance, nonredundancy and the ability to extract features either globally and locally. To assess the applicability of Hahn moments to Face Recognition we conduct two experiments on the Olivetti Research Laboratory (ORL) database and University of Notre-Dame (UND) X1 biometric collection. Fusion of the global features along with the features from local facial regions are used as an input for the conventional k-NN classifier. The method reaches an accuracy of 93% of correctly recognized subjects for the ORL database and 94% for the UND database.

Keywords: Face Recognition, Hahn moments, Recognition-by-parts, Time-lapse.

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Authors: Dewi Nasien, Siti S. Yuhaniz, Habibollah Haron

Abstract:

In a handwriting recognition problem, characters can be represented using chain codes. The main problem in representing characters using chain code is optimizing the length of the chain code. This paper proposes to use randomized algorithm to minimize the length of Freeman Chain Codes (FCC) generated from isolated handwritten characters. Feedforward neural network is used in the classification stage to recognize the image characters. Our test results show that by applying the proposed model, we reached a relatively high accuracy for the problem of isolated handwritten when tested on NIST database.

Keywords: Handwriting Recognition, Freeman Chain Code andFeedforward Backpropagation Neural Networks.

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Authors: Yogita, Durga Toshniwal

Abstract:

Outlier detection in streaming data is very challenging because streaming data cannot be scanned multiple times and also new concepts may keep evolving. Irrelevant attributes can be termed as noisy attributes and such attributes further magnify the challenge of working with data streams. In this paper, we propose an unsupervised outlier detection scheme for streaming data. This scheme is based on clustering as clustering is an unsupervised data mining task and it does not require labeled data, both density based and partitioning clustering are combined for outlier detection. In this scheme partitioning clustering is also used to assign weights to attributes depending upon their respective relevance and weights are adaptive. Weighted attributes are helpful to reduce or remove the effect of noisy attributes. Keeping in view the challenges of streaming data, the proposed scheme is incremental and adaptive to concept evolution. Experimental results on synthetic and real world data sets show that our proposed approach outperforms other existing approach (CORM) in terms of outlier detection rate, false alarm rate, and increasing percentages of outliers.

Keywords: Concept Evolution, Irrelevant Attributes, Streaming Data, Unsupervised Outlier Detection.

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Authors: Mohamed R. Mhereeg

Abstract:

The paper discusses the implementation of the MultiAgent classification System (MACS) and utilizing it to provide an automated and accurate classification of end users developing applications in the spreadsheet domain. However, different technologies have been brought together to build MACS. The strength of the system is the integration of the agent technology with the FIPA specifications together with other technologies, which are the .NET widows service based agents, the Windows Communication Foundation (WCF) services, the Service Oriented Architecture (SOA), and Oracle Data Mining (ODM). The Microsoft's .NET widows service based agents were utilized to develop the monitoring agents of MACS, the .NET WCF services together with SOA approach allowed the distribution and communication between agents over the WWW. The Monitoring Agents (MAs) were configured to execute automatically to monitor excel spreadsheets development activities by content. Data gathered by the Monitoring Agents from various resources over a period of time was collected and filtered by a Database Updater Agent (DUA) residing in the .NET client application of the system. This agent then transfers and stores the data in Oracle server database via Oracle stored procedures for further processing that leads to the classification of the end user developers.

Keywords: MACS, Implementation, Multi-Agent, SOA, Autonomous, WCF.

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Authors: Moung Young Lee, Chul Gyu Song

Abstract:

Photoacoustic imaging is the imaging technology that combines the optical imaging and ultrasound. This provides the high contrast and resolution due to optical imaging and ultrasound imaging, respectively. We developed the real-time photoacoustic tomography (PAT) system using linear-ultrasound transducer and digital acquisition (DAQ) board. There are two types of algorithm for reconstructing the photoacoustic signal. One is back-projection algorithm, the other is FFT algorithm. Especially, we used the non-uniform FFT algorithm. To evaluate the performance of our system and algorithms, we monitored two wires that stands at interval of 2.89 mm and 0.87 mm. Then, we compared the images reconstructed by algorithms. Finally, we monitored the two hairs crossed and compared between these algorithms.

Keywords: Back-projection, image comparison, non-uniform FFT, photoacoustic tomography.

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Authors: Stuart Henry, Philip Morrow, John Winder, Bryan Scotney

Abstract:

The stereophotogrammetry modality is gaining more widespread use in the clinical setting. Registration and visualization of this data, in conjunction with conventional 3D volumetric image modalities, provides virtual human data with textured soft tissue and internal anatomical and structural information. In this investigation computed tomography (CT) and stereophotogrammetry data is acquired from 4 anatomical phantoms and registered using the trimmed iterative closest point (TrICP) algorithm. This paper fully addresses the issue of imaging artifacts around the stereophotogrammetry surface edge using the registered CT data as a reference. Several iterative algorithms are implemented to automatically identify and remove stereophotogrammetry surface edge outliers, improving the overall visualization of the combined stereophotogrammetry and CT data. This paper shows that outliers at the surface edge of stereophotogrammetry data can be successfully removed automatically.

Keywords: Data cleansing, stereophotogrammetry.

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Authors: Muthatakul Metasit, Setthasakko Watchaneeporn

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At present, increased concerns about global environmental problems have magnified the importance of sustainability management. To move towards sustainability, companies need to look at everything from a holistic perspective in order to understand the interconnections between economic growth and environmental and social sustainability. This paper aims to gain an understanding of key determinants that drive sustainability management and barriers that hinder its development. It employs semi-structured interviews with key informants, site observation and documentation. The informants are production, marketing and environmental managers of the leading wine producer, which aims to become an Asia-s leader in wine & wine based products. It is found that corporate image and top management leadership are the primary factors influencing the adoption of sustainability management. Lack of environmental knowledge and inefficient communication are identified as barriers.

Keywords: Environmental, knowledge; Sustainability management; Top management leadership; Wine industry

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Authors: Nikhat Nawaz, S. Srinivasulu, P. Kesava Rao

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Feature extraction plays an important role in many remote sensing applications. Automatic extraction of water bodies is of great significance in many remote sensing applications like change detection, image retrieval etc. This paper presents a procedure for automatic extraction of water information from remote sensing images. The algorithm uses the relative location of R color component of the chromaticity diagram. This method is then integrated with the effectiveness of the spatial scale transformation of whole method. The whole method is based on water index fitted from spectral library. Experimental results demonstrate the improved accuracy and effectiveness of the integrated method for automatic extraction of water bodies.

Keywords: Chromaticity, Feature Extraction, Remote Sensing, Spectral library, Water Index.

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Authors: Seong-Mo An, Rohit Ramesh, Young-Sook Lee, Wan-Young Chung

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The proper assessment of interaxial distance and convergence control are important factors in stereoscopic imaging technology to make an efficient 3D image. To control interaxial distance and convergence for efficient 3D shooting, horizontal 3D camera rig is designed using some hardware components like 'LM Guide', 'Goniometer' and 'Rotation Stage'. The horizontal 3D camera rig system can be properly aligned by moving the two cameras horizontally in same or opposite directions, by adjusting the camera angle and finally considering horizontal swing as well as vertical swing. In this paper, the relationship between interaxial distance and convergence angle control are discussed and intensive experiments are performed in order to demonstrate an easy and effective 3D shooting.

Keywords: Interaxial, Convergence, Stereoscopic, Horizontal 3D Camera Rig

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Authors: Somtop Keawchuer

Abstract:

The objective of this research was to study the factors related to the satisfaction of consumers who purchased a Toyota SUV Fortuner. This paper was a survey data which collected 400 samples from 65 car dealerships. The survey was conducted mainly in Bangkok, Thailand. The statistics utilized in this paper included percentage, mean, standard deviation and Pearson Product-Moment. The findings revealed that the majority of respondent were male with an undergraduate degree, married and live together. The average income of the respondents was between 20,001 - 30,000 baht. Most of them worked for private companies. Most of them had a family with the average of 4 members. The hypotheses testing revealed that the factors of marketing mix in terms of product (ability, gas mileage, and safety) were related to overall satisfaction at the medium level. However, the findings also revealed that the factors of marketing mix in terms of product (image), price, and promotion, and service center were related to the overall satisfaction at the low level.

Keywords: Car Consumers, Factors related, Overall Satisfaction.

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Authors: Dongkoo Yun, Melissa James-MacEachern

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This study analyzes Americans’ views of four Atlantic Canadian provinces as travel destinations regarding specific destination attributes for a pleasure trip, awareness (heard) of the destinations, past visit to the destinations during the prior two years, and intention to visit in the next two years. Results indicate that American travellers perceived the four Atlantic Canadian provinces as separate and distinct when rating best-fit destination attributes to each destination. The results suggest that travel destinations, specifically the four selected destinations, must be prepared to differentiate their destination’s image and the range of experiences and services to appeal and attract more American travellers.

Keywords: Atlantic Canadian provinces (travel destinations), American perceptions, competitiveness, positioning analysis.

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Authors: Changhwan Choi, Yongwan Park

Abstract:

In this paper, we propose a method that allows faster and more accurate detection of traffic lights by a vision sensor during driving, DGPS is used to obtain physical location of a traffic light, extract from the image information of the vision sensor only the traffic light area at this location and ascertain if the sign is in operation and determine its form. This method can solve the problem in existing research where low visibility at night or reflection under bright light makes it difficult to recognize the form of traffic light, thus making driving unstable. We compared our success rate of traffic light recognition in day and night road environments. Compared to previous researches, it showed similar performance during the day but 50% improvement at night.

Keywords: Traffic light, Intelligent vehicle, Night, Detection, DGPS (Differential Global Positioning System).

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Authors: Nebi Gedik

Abstract:

One of the important parts of the brain-computer interface (BCI) studies is the classification of motor imagery (MI) obtained by electroencephalography (EEG). The major goal is to provide non-muscular communication and control via assistive technologies to people with severe motor disorders so that they can communicate with the outside world. In this study, an EEG signal classification approach based on multiscale and multi-resolution transform method is presented. The proposed approach is used to decompose the EEG signal containing motor image information (right- and left-hand movement imagery). The decomposition process is performed using curvelet transform which is a multiscale and multiresolution analysis method, and the transform output was evaluated as feature data. The obtained feature set is subjected to feature selection process to obtain the most effective ones using t-test methods. SVM and k-NN algorithms are assigned for classification.

Keywords: motor imagery, EEG, curvelet transform, SVM, k-NN

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