Search results for: lateral features

Authors: Mohammad Reza Pirestani, Hamid Reza Vosoghifar, Pegah Jazayeri

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In designing river intakes and diversion structures, it is paramount that the sediments entering the intake are minimized or, if possible, completely separated. Due to high water velocity, sediments can significantly damage hydraulic structures especially when mechanical equipment like pumps and turbines are used. This subsequently results in wasting water, electricity and further costs. Therefore, it is prudent to investigate and analyze the performance of lateral intakes affected by sediment control structures. Laboratory experiments, despite their vast potential and benefits, can face certain limitations and challenges. Some of these include: limitations in equipment and facilities, space constraints, equipment errors including lack of adequate precision or mal-operation, and finally, human error. Research has shown that in order to achieve the ultimate goal of intake structure design – which is to design longlasting and proficient structures – the best combination of sediment control structures (such as sill and submerged vanes) along with parameters that increase their performance (such as diversion angle and location) should be determined. Cost, difficulty of execution and environmental impacts should also be included in evaluating the optimal design. This solution can then be applied to similar problems in the future. Subsequently, the model used to arrive at the optimal design requires high level of accuracy and precision in order to avoid improper design and execution of projects. Process of creating and executing the design should be as comprehensive and applicable as possible. Therefore, it is important that influential parameters and vital criteria is fully understood and applied at all stages of choosing the optimal design. In this article, influential parameters on optimal performance of the intake, advantages and disadvantages, and efficiency of a given design are studied. Then, a multi-criterion decision matrix is utilized to choose the optimal model that can be used to determine the proper parameters in constructing the intake.

Keywords: Diversion structures lateral intake, multi criteria decision making, optimal design, sediment control.

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Authors: Songtao Wu, Yuesheng Zhu, Ziqiang Sun

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Region covariance (RC) descriptor is an effective and efficient feature for visual tracking. Current RC-based tracking algorithms use the whole RC matrix to track the target in video directly. However, there exist some issues for these whole RCbased algorithms. If some features are contaminated, the whole RC will become unreliable, which results in lost object-tracking. In addition, if some features are very discriminative to the background, other features are still processed and thus reduce the efficiency. In this paper a new robust tracking method is proposed, in which the whole RC matrix is decomposed into several low rank matrices. Those matrices are dynamically chosen and processed so as to achieve a good tradeoff between discriminability and complexity. Experimental results have shown that our method is more robust to complex environment changes, especially either when occlusion happens or when the background is similar to the target compared to other RC-based methods.

Keywords: Visual tracking, region covariance descriptor, lowrankregion covariance

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Authors: W. Y. Leng, S. M. Shamsuddin

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Fingerprint based identification system; one of a well known biometric system in the area of pattern recognition and has always been under study through its important role in forensic science that could help government criminal justice community. In this paper, we proposed an identification framework of individuals by means of fingerprint. Different from the most conventional fingerprint identification frameworks the extracted Geometrical element features (GEFs) will go through a Discretization process. The intention of Discretization in this study is to attain individual unique features that could reflect the individual varianceness in order to discriminate one person from another. Previously, Discretization has been shown a particularly efficient identification on English handwriting with accuracy of 99.9% and on discrimination of twins- handwriting with accuracy of 98%. Due to its high discriminative power, this method is adopted into this framework as an independent based method to seek for the accuracy of fingerprint identification. Finally the experimental result shows that the accuracy rate of identification of the proposed system using Discretization is 100% for FVC2000, 93% for FVC2002 and 89.7% for FVC2004 which is much better than the conventional or the existing fingerprint identification system (72% for FVC2000, 26% for FVC2002 and 32.8% for FVC2004). The result indicates that Discretization approach manages to boost up the classification effectively, and therefore prove to be suitable for other biometric features besides handwriting and fingerprint.

Keywords: Discretization, fingerprint identification, geometrical features, pattern recognition

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Authors: Hendry Muljadi, Hideaki Takeda, Koichi Ando

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A manufacturing feature can be defined simply as a geometric shape and its manufacturing information to create the shape. In a feature-based process planning system, feature library that consists of pre-defined manufacturing features and the manufacturing information to create the shape of the features, plays an important role in the extraction of manufacturing features with their proper manufacturing information. However, to manage the manufacturing information flexibly, it is important to build a feature library that can be easily modified. In this paper, the implementation of Semantic Wiki for the development of the feature library is proposed.

Keywords: Manufacturing feature, feature library, feature ontology, process planning, Wiki, MediaWiki, Semantic Wiki.

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Authors: M. V. Sudhamani, C. R. Venugopal

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Content-based Image Retrieval (CBIR) aims at searching image databases for specific images that are similar to a given query image based on matching of features derived from the image content. This paper focuses on a low-dimensional color based indexing technique for achieving efficient and effective retrieval performance. In our approach, the color features are extracted using the mean shift algorithm, a robust clustering technique. Then the cluster (region) mode is used as representative of the image in 3-D color space. The feature descriptor consists of the representative color of a region and is indexed using a spatial indexing method that uses *R -tree thus avoiding the high-dimensional indexing problems associated with the traditional color histogram. Alternatively, the images in the database are clustered based on region feature similarity using Euclidian distance. Only representative (centroids) features of these clusters are indexed using *R -tree thus improving the efficiency. For similarity retrieval, each representative color in the query image or region is used independently to find regions containing that color. The results of these methods are compared. A JAVA based query engine supporting query-by- example is built to retrieve images by color.

Keywords: Content-based, indexing, cluster, region.

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Authors: B. Leela Devi, A. Sankar

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The web’s increased popularity has included a huge amount of information, due to which automated web page classification systems are essential to improve search engines’ performance. Web pages have many features like HTML or XML tags, hyperlinks, URLs and text contents which can be considered during an automated classification process. It is known that Webpage classification is enhanced by hyperlinks as it reflects Web page linkages. The aim of this study is to reduce the number of features to be used to improve the accuracy of the classification of web pages. In this paper, a novel feature selection method using an improved Particle Swarm Optimization (PSO) using principle of evolution is proposed. The extracted features were tested on the WebKB dataset using a parallel Neural Network to reduce the computational cost.

Keywords: Web page classification, WebKB Dataset, Term Frequency-Inverse Document Frequency (TF-IDF), Particle Swarm Optimization (PSO).

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Authors: A. Mohammadi Torkashvand, N. Haghighat

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Some methodologies were compared in providing erosion maps of surface, rill and gully and erosion features, in research which took place in the Varamin sub-basin, north-east Tehran, Iran. A photomorphic unit map was produced from processed satellite images, and four other maps were prepared by the integration of different data layers, including slope, plant cover, geology, land use, rocks erodibility and land units. Comparison of ground truth maps of erosion types and working unit maps indicated that the integration of land use, land units and rocks erodibility layers with satellite image photomorphic units maps provide the best methods in producing erosion types maps.

Keywords: Erosion Features, Geographic Information System, Remote Sensing.

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Authors: Mangesh S. Deshpande, Raghunath S. Holambe

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Mel Frequency Cepstral Coefficient (MFCC) features are widely used as acoustic features for speech recognition as well as speaker recognition. In MFCC feature representation, the Mel frequency scale is used to get a high resolution in low frequency region, and a low resolution in high frequency region. This kind of processing is good for obtaining stable phonetic information, but not suitable for speaker features that are located in high frequency regions. The speaker individual information, which is non-uniformly distributed in the high frequencies, is equally important for speaker recognition. Based on this fact we proposed an admissible wavelet packet based filter structure for speaker identification. Multiresolution capabilities of wavelet packet transform are used to derive the new features. The proposed scheme differs from previous wavelet based works, mainly in designing the filter structure. Unlike others, the proposed filter structure does not follow Mel scale. The closed-set speaker identification experiments performed on the TIMIT database shows improved identification performance compared to other commonly used Mel scale based filter structures using wavelets.

Keywords: Speaker identification, Wavelet transform, Feature extraction, MFCC, GMM.

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Authors: H. Meddah, M. Berediaf-Bourahla, B. El-Djouzi, N. Bourahla

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Shear walls made of cold formed steel are used as lateral force resisting components in residential and low-rise commercial and industrial constructions. The seismic design analysis of such structures is often complex due to the slenderness of members and their instability prevalence. In this context, a simplified modeling technique across the panel is proposed by using the finite element method. The approach is based on idealizing the whole panel by a nonlinear shear link element which reflects its shear behavior connected to rigid body elements which transmit the forces to the end elements (studs) that resist the tension and the compression. The numerical model of the shear wall panel was subjected to cyclic loads in order to evaluate the seismic performance of the structure in terms of lateral displacement and energy dissipation capacity. In order to validate this model, the numerical results were compared with those from literature tests. This modeling technique is particularly useful for the design of cold formed steel structures where the shear forces in each panel and the axial forces in the studs can be obtained using spectrum analysis.

Keywords: Cold-formed steel, cyclic loading, modeling technique, nonlinear analysis, shear wall panel.

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Authors: M. V. Sudhamani, C. R. Venugopal

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This paper deals with the application for contentbased image retrieval to extract color feature from natural images stored in the image database by segmenting the image through clustering. We employ a class of nonparametric techniques in which the data points are regarded as samples from an unknown probability density. Explicit computation of the density is avoided by using the mean shift procedure, a robust clustering technique, which does not require prior knowledge of the number of clusters, and does not constrain the shape of the clusters. A non-parametric technique for the recovery of significant image features is presented and segmentation module is developed using the mean shift algorithm to segment each image. In these algorithms, the only user set parameter is the resolution of the analysis and either gray level or color images are accepted as inputs. Extensive experimental results illustrate excellent performance.

Keywords: Segmentation, Clustering, Image Retrieval, Features.

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Authors: Feifei Lee, Qiu Chen, Koji Kotani, Tadahiro Ohmi

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In this paper, we propose an improved fast search algorithm using combined histogram features and temporal division method for short MPEG video clips from large video database. There are two types of histogram features used to generate more robust features. The first one is based on the adjacent pixel intensity difference quantization (APIDQ) algorithm, which had been reliably applied to human face recognition previously. An APIDQ histogram is utilized as the feature vector of the frame image. Another one is ordinal feature which is robust to color distortion. Combined with active search [4], a temporal pruning algorithm, fast and robust video search can be realized. The proposed search algorithm has been evaluated by 6 hours of video to search for given 200 MPEG video clips which each length is 30 seconds. Experimental results show the proposed algorithm can detect the similar video clip in merely 120ms, and Equal Error Rate (ERR) of 1% is achieved, which is more accurately and robust than conventional fast video search algorithm.

Keywords: Fast search, Adjacent pixel intensity differencequantization (APIDQ), DC image, Histogram feature.

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Authors: N. Ghatwary, A. Ahmed, H. Jalab

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In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.

Keywords: Fractional differential (FD), Computed Tomography (CT), fusion.

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Authors: Lavanya Srinivasan

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Biometric identification is to identify unique features in a person like fingerprints, iris, ear, and voice recognition that need the subject's permission and physical contact. Gait biometric is used to identify the unique gait of the person by extracting moving features. The main advantage of gait biometric to identify the gait of a person at a distance, without any physical contact. In this work, the gait biometric is used for person re-identification. The person walking naturally compared with the same person walking with bag, coat and case recorded using long wave infrared, short wave infrared, medium wave infrared and visible cameras. The videos are recorded in rural and in urban environments. The pre-processing technique includes human identified using You Only Look Once, background subtraction, silhouettes extraction and synthesis Gait Entropy Image by averaging the silhouettes. The moving features are extracted from the Gait Entropy Energy Image. The extracted features are dimensionality reduced by the Principal Component Analysis and recognized using different classifiers. The comparative results with the different classifier show that Linear Discriminant Analysis outperform other classifiers with 95.8% for visible in the rural dataset and 94.8% for longwave infrared in the urban dataset.

Keywords: biometric, gait, silhouettes, You Only Look Once

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Authors: Chan Weng Howe, Mohd Saberi Mohamad

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Protein residue contact map is a compact representation of secondary structure of protein. Due to the information hold in the contact map, attentions from researchers in related field were drawn and plenty of works have been done throughout the past decade. Artificial intelligence approaches have been widely adapted in related works such as neural networks, genetic programming, and Hidden Markov model as well as support vector machine. However, the performance of the prediction was not generalized which probably depends on the data used to train and generate the prediction model. This situation shown the importance of the features or information used in affecting the prediction performance. In this research, support vector machine was used to predict protein residue contact map on different combination of features in order to show and analyze the effectiveness of the features.

Keywords: contact map, protein residue contact, support vector machine, protein structure prediction

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Authors: Baljit Singh Khehra, Amar Partap Singh Pharwaha

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Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.

Keywords: Clusters of Microcalcifications, Ductal Carcinoma in Situ, Least-Square Support Vector Machine, Particle Swarm Optimization.

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Authors: Essam Al-Daoud

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A New features are extracted and compared to improve the prediction of protein-protein interactions. The basic idea is to select and use the best set of features from the Tensor matrices that are produced by the frequency vectors of the protein sequences. Three set of features are compared, the first set is based on the indices that are the most common in the interacting proteins, the second set is based on the indices that tend to be common in the interacting and non-interacting proteins, and the third set is constructed by using random indices. Moreover, three encoding strategies are compared; that are based on the amino asides polarity, structure, and chemical properties. The experimental results indicate that the highest accuracy can be obtained by using random indices with chemical properties encoding strategy and support vector machine.

Keywords: protein-protein interactions, random indices, encoding strategies, support vector machine.

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Authors: N. Valliammal, S.N. Geethalakshmi

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This paper describes an optimal approach for feature subset selection to classify the leaves based on Genetic Algorithm (GA) and Kernel Based Principle Component Analysis (KPCA). Due to high complexity in the selection of the optimal features, the classification has become a critical task to analyse the leaf image data. Initially the shape, texture and colour features are extracted from the leaf images. These extracted features are optimized through the separate functioning of GA and KPCA. This approach performs an intersection operation over the subsets obtained from the optimization process. Finally, the most common matching subset is forwarded to train the Support Vector Machine (SVM). Our experimental results successfully prove that the application of GA and KPCA for feature subset selection using SVM as a classifier is computationally effective and improves the accuracy of the classifier.

Keywords: Optimization, Feature extraction, Feature subset, Classification, GA, KPCA, SVM and Computation

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Authors: M.Ghazvini, S. A. Monadjemi, N. Movahhedinia, K. Jamshidi

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In this article, a method has been offered to classify normal and defective tiles using wavelet transform and artificial neural networks. The proposed algorithm calculates max and min medians as well as the standard deviation and average of detail images obtained from wavelet filters, then comes by feature vectors and attempts to classify the given tile using a Perceptron neural network with a single hidden layer. In this study along with the proposal of using median of optimum points as the basic feature and its comparison with the rest of the statistical features in the wavelet field, the relational advantages of Haar wavelet is investigated. This method has been experimented on a number of various tile designs and in average, it has been valid for over 90% of the cases. Amongst the other advantages, high speed and low calculating load are prominent.

Keywords: Defect detection, tile and ceramic quality inspection, wavelet transform, classification, neural networks, statistical features.

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Authors: Tomyslav Sledevič, Artūras Serackis, Gintautas Tamulevičius, Dalius Navakauskas

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Paper presents an comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in speaker dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signal to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients gives best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfy the real-time requirements and is suitable for applications in embedded systems.

Keywords: Isolated word recognition, features extraction, MFCC, LFCC, LPCC, LPC, FPGA, DTW.

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Authors: Zasiah Tafheem, Mahadee Hasan Shourav, Zahidul Islam, Saima Islam Tumpa

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The paper aims at investigating the effect of number of story on different structural components of reinforced concrete building due to gravity and lateral loading. For the study, three building models having same building plan of three, six and nine stories are analyzed and designed using software package. All the buildings are residential and are located in Dhaka city of Bangladesh. Lateral load including wind and earthquake loading are applied to the building along both longitudinal and transverse direction as per Bangladesh National Building Code (BNBC, 2006). Equivalent static force method is followed for the applied seismic loading. The present study investigates as well as compares mainly total steel requirement in different structural components for those buildings. It has been found that total longitudinal steel requirement for beams at each floor is 48.57% for three storied building, 61.36% for six storied building when the total percentage is taken as 100% in case of nine storied building. For an exterior column, the steel ratio is 2.1%, 3.06%, 4.55% for three, six and nine storied building respectively for the first three floors. In addition, it has been noted that total weight of longitudinal reinforcement of an interior column is 14.02 % for threestoried building and 43.12% for six storied building when the total reinforcement is considered 100% for nine storied building for the first three floors.

Keywords: Equivalent Static Force Method, longitudinal reinforcement, seismic loading, steel ratio.

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Authors: Mohamed Abdel Fattah, Fuji Ren

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This work proposes an approach to address automatic text summarization. This approach is a trainable summarizer, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity, sentence inclusion of numerical data, sentence relative length, Bushy path of the sentence and aggregated similarity for each sentence to generate summaries. First we investigate the effect of each sentence feature on the summarization task. Then we use all features score function to train genetic algorithm (GA) and mathematical regression (MR) models to obtain a suitable combination of feature weights. The proposed approach performance is measured at several compression rates on a data corpus composed of 100 English religious articles. The results of the proposed approach are promising.

Keywords: Automatic Summarization, Genetic Algorithm, Mathematical Regression, Text Features.

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Authors: Muhibul Haque Bhuyan, Farseem Mannan Mohammedy, Quazi Deen Mohd Khosru

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This paper presents the doping profile measurement and characterization technique for the pocket implanted nano scale n-MOSFET. Scanning capacitance microscopy and atomic force microscopy have been used to image the extent of lateral dopant diffusion in MOS structures. The data are capacitance vs. voltage measurements made on a nano scale device. The technique is nondestructive when imaging uncleaved samples. Experimental data from the published literature are presented here on actual, cleaved device structures which clearly indicate the two-dimensional dopant profile in terms of a spatially varying modulated capacitance signal. Firstorder deconvolution indicates the technique has much promise for the quantitative characterization of lateral dopant profiles. The pocket profile is modeled assuming the linear pocket profiles at the source and drain edges. From the model, the effective doping concentration is found to use in modeling and simulation results of the various parameters of the pocket implanted nano scale n-MOSFET. The potential of the technique to characterize important device related phenomena on a local scale is also discussed.

Keywords: Linear Pocket Profile, Pocket Implanted n-MOSFET, Scanning Capacitance Microscope, Atomic Force Microscope.

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Authors: Dinesh Acharya U., N. V. Subba Reddy, Krishnamoorthi Makkithaya

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The recognition of handwritten numeral is an important area of research for its applications in post office, banks and other organizations. This paper presents automatic recognition of handwritten Kannada numerals based on structural features. Five different types of features, namely, profile based 10-segment string, water reservoir; vertical and horizontal strokes, end points and average boundary length from the minimal bounding box are used in the recognition of numeral. The effect of each feature and their combination in the numeral classification is analyzed using nearest neighbor classifiers. It is common to combine multiple categories of features into a single feature vector for the classification. Instead, separate classifiers can be used to classify based on each visual feature individually and the final classification can be obtained based on the combination of separate base classification results. One popular approach is to combine the classifier results into a feature vector and leaving the decision to next level classifier. This method is extended to extract a better information, possibility distribution, from the base classifiers in resolving the conflicts among the classification results. Here, we use fuzzy k Nearest Neighbor (fuzzy k-NN) as base classifier for individual feature sets, the results of which together forms the feature vector for the final k Nearest Neighbor (k-NN) classifier. Testing is done, using different features, individually and in combination, on a database containing 1600 samples of different numerals and the results are compared with the results of different existing methods.

Keywords: Fuzzy k Nearest Neighbor, Multiple Classifiers, Numeral Recognition, Structural features.

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Authors: Kwangjin Hong, Chulhan Lee, Keechul Jung, Kyoungsu Oh

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For the communication between human and computer in an interactive computing environment, the gesture recognition is studied vigorously. Therefore, a lot of studies have proposed efficient methods about the recognition algorithm using 2D camera captured images. However, there is a limitation to these methods, such as the extracted features cannot fully represent the object in real world. Although many studies used 3D features instead of 2D features for more accurate gesture recognition, the problem, such as the processing time to generate 3D objects, is still unsolved in related researches. Therefore we propose a method to extract the 3D features combined with the 3D object reconstruction. This method uses the modified GPU-based visual hull generation algorithm which disables unnecessary processes, such as the texture calculation to generate three kinds of 3D projection maps as the 3D feature: a nearest boundary, a farthest boundary, and a thickness of the object projected on the base-plane. In the section of experimental results, we present results of proposed method on eight human postures: T shape, both hands up, right hand up, left hand up, hands front, stand, sit and bend, and compare the computational time of the proposed method with that of the previous methods.

Keywords: Fast 3D Feature Extraction, Gesture Recognition, Computer Vision.

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Authors: Peter A. Bibby

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Research has suggested that implicit learning tasks may rely on episodic processing to generate above chance performance on the standard classification tasks. The current research examines the invariant features task (McGeorge and Burton, 1990) and argues that such episodic processing is indeed important. The results of the experiment suggest that both rejection and similarity strategies are used by participants in this task to simultaneously reject unfamiliar items and to accept (falsely) familiar items. Primarily these decisions are based on the presence of low or high frequency goal based features of the stimuli presented in the incidental learning phase. It is proposed that a goal based analysis of the incidental learning task provides a simple step in understanding which features of the episodic processing are most important for explaining the match between incidental, implicit learning and test performance.

Keywords: Episodic processing, incidental learning, implicitlearning, invariant learning.

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Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

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The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the supportvectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: Discrete Wavelet Transform, Electroencephalogram, Pattern Recognition, Principal Component Analysis, Support Vector Machine.

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Authors: Shu-Lu Hsu, Jeffrey C. F. Tai, Yi-Han Wang

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The purpose of this study is to understand how consumers react to a company's promotional offers with mobile applications (APP) as premiums. This paper presents the results of an experimental study where five features of APP were involved: the cost (free/discounted) for earning APP, the relationship between APP and the promoted product, the perceived usefulness, the perceived ease of use, and the perceived playfulness of APP in the context of light foods purchase. The results support that the above features, except perceived ease of use, have substantial influences on consumers' intention to adopt the APP. Among the five features, the cost for earning APP has the most impact on the adopting intention of APP. The study also found a positive influence of adopting intention of APP on the consumer's purchase intention of the promoted product. Thus, APP-based premiums may enhance the consumer's purchase intention of a company's promoted products.

Keywords: Mobile Application, Premium, Sales Promotion, TAM.

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Authors: Mohamed Taher Eldesoky, Enas Elsayed Abutaleb

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Background: The change in foot posture can possibly generate changes in the pelvic alignment. There is still a lack of evidence about the effects of bilateral and unilateral flatfoot on possible changes in pelvic alignment. The purpose of this study was to investigate the effect of flatfoot on the sagittal and frontal planes of pelvic postures. Materials and Methods: 56 subjects, aged 18–40 years, were assigned into three groups: 20 healthy subjects, 19 subjects with bilateral flexible second-degree flat foot, and 17 subjects with unilateral flexible second-degree flat foot. 3D assessment of the pelvis using the formetric-II device was used to evaluate pelvic alignment in the frontal and sagittal planes by measuring pelvic inclination and pelvic tilt angles. Results: ANOVA test with LSD test were used for statistical analysis. Both Unilateral and bilateral second degree flatfoot produced significant (P<0.05) pelvic anteversion, in comparison to the healthy subjects (P<0.05). But the bilateral flatfoot subjects seemed to have more anteversion than the unilateral subjects. Unilateral flatfoot caused a significant (P<0.05) lateral pelvic tilt in the direction of the affected side in comparison to the healthy and bilateral flatfoot subjects. Conclusion: The bilateral and unilateral second degree flatfoot changes pelvic alignment. Both of them lead to increases of pelvic anteversion while the unilateral one caused lateral pelvic tilt toward the affected side. Thus, foot posture should be considered when assessing patients with pelvic misalignment and disorders.

Keywords: Bilateral flatfoot, foot posture, pelvic alignment, unilateral flatfoot.

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Authors: Mahesh Pal

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This paper proposes to use ETM+ multispectral data and panchromatic band as well as texture features derived from the panchromatic band for land cover classification. Four texture features including one 'internal texture' and three GLCM based textures namely correlation, entropy, and inverse different moment were used in combination with ETM+ multispectral data. Two data sets involving combination of multispectral, panchromatic band and its texture were used and results were compared with those obtained by using multispectral data alone. A decision tree classifier with and without boosting were used to classify different datasets. Results from this study suggest that the dataset consisting of panchromatic band, four of its texture features and multispectral data was able to increase the classification accuracy by about 2%. In comparison, a boosted decision tree was able to increase the classification accuracy by about 3% with the same dataset.

Keywords: Internal texture; GLCM; decision tree; boosting; classification accuracy.

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

Abstract:

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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