Search results for: low/high level features

Authors: Hatice Odacı

Abstract:

Depression is a serious mental health problem that affects people of all ages, including children and adolescents. Studies showed that female gender is one of the risk factors may influence the development of depression in adolescents. However, some of the studies from Turkey suggested that gender does not lead to any significant difference in the youth depression level. Therefore, the presented study investigated whether girls differ from boys in respect of depression. The association between genders and test scores for the adolescents in a population of primary and secondary school students was also evaluated. The study was consisting of 254 adolescents (122 boys and 132 girls) with a mean age of 13.86±1.43 (Mean±SD) ranging from 12-16 years. Psychological assessment was performed using Children-s Depression Inventory (CDI). Chi-square and Student-s t-test statistics were employed to analyze the data. The mean of the CDI scores of the girls were higher than boys- CDI scores (t = -4.580, p = 0.001). Higher ratio appeared for the girls when they compared with boy group-s depression levels using a CDI cut-off point of 19 (p = 0.001, Odds Ratio = 2,603). The findings of the present study suggested that adolescent girls have high level of depression than adolescent boys aged between 12-16 years in Turkey. Although some studies reported that there is no any differences depression level between adolescent boys and girls in Turkey, result of the present study showed that adolescent girls have high level of depression than adolescent boys in Turkey.

Keywords: Depression, Adolescent, Turkey, Female Gender, Male Gender.

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Authors: Woo-Young Jung, Minho Kwon

Abstract:

Seismic design criteria based on performance of structures have recently been adopted by practicing engineers in response to destructive earthquakes. A simple but efficient structural-analysis tool capable of predicting both the strength and ductility is needed to analyze reinforced concrete (RC) structures under such event. A three-dimensional lattice model is developed in this study to analyze torsions in high-strength RC members. Optimization techniques for determining optimal variables in each lattice model are introduced. Pure torsion tests of RC members are performed to validate the proposed model. Correlation studies between the numerical and experimental results confirm that the proposed model is well capable of representing salient features of the experimental results.

Keywords: Torsion, non-linear analysis, three-dimensional lattice, high-strength concrete.

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Authors: Seongykyoon Jeong, Sungki Lee, Jaeyun Kim, Seunghun Oh, Kiho Kwak

Abstract:

The purpose of study is to demonstrate how the characteristics of technology and the process required for development of technology affect technology transfer from public organisations to industry on the technology level. In addition, using the advantage of the analytic level and the novel means of measuring technology convergence, we examine the characteristics of converging technologies as compared to non-converging technologies in technology transfer process. In sum, our study finds that a technology from the public sector is likely to be transferred when its readiness level is closer to generation of profit, when its stage of life cycle is early and when its economic values is high. Our findings also show that converging technologies are less likely to be transferred.

Keywords: Interdisciplinary, Technology transfer, Technology convergence.

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Authors: A. Bouzid, R. Chadli

Abstract:

Fritillaria oranensis (Liliaceae) was described in 1874 by pomel from Algeria. Plant samples have been collected from the mount of Tessala (Sidi-Bel-Abbes). The morphological features of various organs of the plant are described in detail. In the morphological part of the study, features of various organs of the plants such as stem and leaf were determined and illustrated. Ecological studies provide information about the physical and chemical structure of soil types in Tessala Mountain. The aim of this original investigation is to put forth ecological and anatomical features of these species for the first time, but at the same time given detailed account of the morphological characteristics of the stem and leaf of Fritillaria oranensis.

Keywords: Anatomy, ecology, Liliaceae, morphology, Fritillaria oranensis Pomel.

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Authors: Saeed Mian Qaisar, Laurent Fesquet, Marc Renaudin

Abstract:

The frequency contents of the non-stationary signals vary with time. For proper characterization of such signals, a smart time-frequency representation is necessary. Classically, the STFT (short-time Fourier transform) is employed for this purpose. Its limitation is the fixed timefrequency resolution. To overcome this drawback an enhanced STFT version is devised. It is based on the signal driven sampling scheme, which is named as the cross-level sampling. It can adapt the sampling frequency and the window function (length plus shape) by following the input signal local variations. This adaptation results into the proposed technique appealing features, which are the adaptive time-frequency resolution and the computational efficiency.

Keywords: Level Crossing Sampling, Activity Selection, Adaptive Resolution Analysis, Computational Complexity.

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Authors: L. Lakhdari, B. Bouchiba, M. Bechar

Abstract:

The multi-level inverters present an important novelty in the field of energy control with high voltage and power. The major advantage of all multi-level inverters is the improvement and spectral quality of its generated output signals. In recent years, various pulse width modulation techniques have been developed. From these technics we have: Sinusoidal Pulse Width Modulation (SPWM) and Space Vector Pulse Width Modulation (SVPWM). This work presents a detailed analysis of the comparative advantage of space vector pulse width modulation (SVPWM) and the standard SPWM technique for Three Level Diode Clamped Inverter fed Induction Motor. The comparison is based on the evaluation of harmonic distortion THD.

Keywords: Induction motor, multi-level inverters, NPC inverter, sinusoidal pulse width modulation, space vector pulse width modulation.

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Authors: Mikulas Patocka

Abstract:

This paper presents an architecture of current filesystem implementations as well as our new filesystem SpadFS and operating system Spad with rewritten VFS layer targeted at high performance I/O applications. The paper presents microbenchmarks and real-world benchmarks of different filesystems on the same kernel as well as benchmarks of the same filesystem on different kernels – enabling the reader to make conclusion how much is the performance of various tasks affected by operating system and how much by physical layout of data on disk. The paper describes our novel features–most notably continuous allocation of directories and cross-file readahead – and shows their impact on performance.

Keywords: Filesystem, operating system, VFS, performance, readahead

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Authors: Sameerchand Pudaruth, Avinash Mantaye

Abstract:

E-learning refers to the specific kind of learning experienced within the domain of educational technology, which can be used in or out of the classroom. In this paper, we give an overview of an e-learning platform 'An Innovative Interactive and Online English Platform for Upper Primary Students' is an interactive web-based application which will serve as an aid to the primary school students in Mauritius. The objectives of this platform are to offer quality learning resources for the English subject at our primary level of education, encourage self-learning and hence promote e-learning. The platform developed consists of several interesting features, for example, the English Verb Conjugation tool, Negative Form tool, Interrogative Form tool and Close Test Generator. Thus, this learning platform will be useful at a time where our country is looking for an alternative to private tuition and also, looking forward to increase the pass rate.

Keywords: educational technology, e-learning, Mauritius.

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Authors: Dima Stopel, Zvi Boger, Robert Moskovitch, Yuval Shahar, Yuval Elovici

Abstract:

Computer worm detection is commonly performed by antivirus software tools that rely on prior explicit knowledge of the worm-s code (detection based on code signatures). We present an approach for detection of the presence of computer worms based on Artificial Neural Networks (ANN) using the computer's behavioral measures. Identification of significant features, which describe the activity of a worm within a host, is commonly acquired from security experts. We suggest acquiring these features by applying feature selection methods. We compare three different feature selection techniques for the dimensionality reduction and identification of the most prominent features to capture efficiently the computer behavior in the context of worm activity. Additionally, we explore three different temporal representation techniques for the most prominent features. In order to evaluate the different techniques, several computers were infected with five different worms and 323 different features of the infected computers were measured. We evaluated each technique by preprocessing the dataset according to each one and training the ANN model with the preprocessed data. We then evaluated the ability of the model to detect the presence of a new computer worm, in particular, during heavy user activity on the infected computers.

Keywords: Artificial Neural Networks, Feature Selection, Temporal Analysis, Worm Detection.

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Authors: Ekachai Phaisangittisagul, Rapeepol Chongprachawat

Abstract:

Obtaining labeled data in supervised learning is often difficult and expensive, and thus the trained learning algorithm tends to be overfitting due to small number of training data. As a result, some researchers have focused on using unlabeled data which may not necessary to follow the same generative distribution as the labeled data to construct a high-level feature for improving performance on supervised learning tasks. In this paper, we investigate the impact of the relationship between unlabeled and labeled data for classification performance. Specifically, we will apply difference unlabeled data which have different degrees of relation to the labeled data for handwritten digit classification task based on MNIST dataset. Our experimental results show that the higher the degree of relation between unlabeled and labeled data, the better the classification performance. Although the unlabeled data that is completely from different generative distribution to the labeled data provides the lowest classification performance, we still achieve high classification performance. This leads to expanding the applicability of the supervised learning algorithms using unsupervised learning.

Keywords: Autoencoder, high-level feature, MNIST dataset, selftaught learning, supervised learning.

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Authors: Guo Xiuhua, Sun Tao, Wu Haifeng, He Wen, Liang Zhigang, Zhang Mengxia, Guo Aimin, Wang Wei

Abstract:

Purpose: To explore the use of Curvelet transform to extract texture features of pulmonary nodules in CT image and support vector machine to establish prediction model of small solitary pulmonary nodules in order to promote the ratio of detection and diagnosis of early-stage lung cancer. Methods: 2461 benign or malignant small solitary pulmonary nodules in CT image from 129 patients were collected. Fourteen Curvelet transform textural features were as parameters to establish support vector machine prediction model. Results: Compared with other methods, using 252 texture features as parameters to establish prediction model is more proper. And the classification consistency, sensitivity and specificity for the model are 81.5%, 93.8% and 38.0% respectively. Conclusion: Based on texture features extracted from Curvelet transform, support vector machine prediction model is sensitive to lung cancer, which can promote the rate of diagnosis for early-stage lung cancer to some extent.

Keywords: CT image, Curvelet transform, Small pulmonary nodules, Support vector machines, Texture extraction.

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Authors: Jameela Ali Alkrimi, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

Abstract:

Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithms tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital - Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-Nearest Neighbors, Neural Network, Radial Basis Function, Red blood cells, Support vector machine.

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Authors: Walid MOUDANI

Abstract:

The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the Coronary Heart Disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts- knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: Multi-Classifier Decisions Tree, Features Reduction, Dynamic Programming, Rough Sets.

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Authors: Chanpen Meenakorn

Abstract:

This research aims to study; (1) behavior of the tourists affecting the satisfaction level of tourism at the Amphawa floating market in Samut Songkhram province, (2) to study the satisfaction level of tourism at the Amphawa floating market. The research method will use quantitative research; data was collected by questionnaires distributed to the tourist who visits the Amphawa floating market for 480 samples. Data was analyzed by SPSS software to process descriptive statistic including frequency, percentage, mean, standard deviation and inferential statistic is t-test, F-test, and chi-square. The results showed that the behavior of tourists had known tourist attractions in the province comes from the mouth of relatives and friends suggested that he come here before and the reasons to visit is to want to pay homage to the various temples for the frequency to visit travel an average of 2-4 times and  the satisfaction of the tourists in the province found that the satisfaction level of tourists in the province at the significant level of the place, convenient  and services have a high level of satisfaction.

Keywords: Amphawa floating market behavior of the tourists, satisfaction level, sustainable tourism, Samut Songkhram province.

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Authors: Nittaya Kerdprasop, Kittisak Kerdprasop

Abstract:

Frequent patterns are patterns such as sets of features or items that appear in data frequently. Finding such frequent patterns has become an important data mining task because it reveals associations, correlations, and many other interesting relationships hidden in a dataset. Most of the proposed frequent pattern mining algorithms have been implemented with imperative programming languages such as C, Cµ, Java. The imperative paradigm is significantly inefficient when itemset is large and the frequent pattern is long. We suggest a high-level declarative style of programming using a functional language. Our supposition is that the problem of frequent pattern discovery can be efficiently and concisely implemented via a functional paradigm since pattern matching is a fundamental feature supported by most functional languages. Our frequent pattern mining implementation using the Haskell language confirms our hypothesis about conciseness of the program. The performance studies on speed and memory usage support our intuition on efficiency of functional language.

Keywords: Association, frequent pattern mining, functionalprogramming, pattern matching.

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Authors: Jihyung Kim, Taejin Kim, Sungho Park, Jun-Dong Cho

Abstract:

In this paper, the problem of reducing switching activity in on-chip buses at the stage of high-level synthesis is considered, and a high-level low power bus binding based on dynamic bit reordering is proposed. Whereas conventional methods use a fixed bit ordering between variables within a bus, the proposed method switches a bit ordering dynamically to obtain a switching activity reduction. As a result, the proposed method finds a binding solution with a smaller value of total switching activity (TSA). Experimental result shows that the proposed method obtains a binding solution having 12.0-34.9% smaller TSA compared with the conventional methods.

Keywords: bit reordering, bus binding, low power, switching activity matrix

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Authors: Tatiana V. Raudina, Sergey P. Kulizhskiy

Abstract:

A large part of Russia is located in permafrost areas. These areas are widely used because there are concentrated valuable natural resources. Therefore to explore of cryosols it is important due to the significant increase of anthropogenic stress as well as the problem of global climate change. In the north of Western Siberia permafrost phenomena is widespread. Permafrost as a factor of soil formation and cryogenesis as a process have a great impact on the soil formation of these areas. Based on the research results of permafrost-affected soils tundra landscapes formed in the central part of the Tazovskiy Peninsula in cryogenic conditions, data were obtained which characterize the morphological features of soils. The specificity of soil cover distribution and manifestation of soil-forming processes within the study area are noted. Permafrost features such as frost cracking, cryoturbation, thixotropy, movement of humus are formed. The formation of these features is increased with the development of the territory. As a consequence, there is a change in the components of the environment and the destruction of the soil cover.

Keywords: Gleyed and nongleyed soils, permafrost, soil cryogenesis (pedocryogenesis), soil-forming macroprocesses.

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Authors: Maryam Mirdehghani, S. Amirhassan Monadjemi

Abstract:

Web sites are rapidly becoming the preferred media choice for our daily works such as information search, company presentation, shopping, and so on. At the same time, we live in a period where visual appearances play an increasingly important role in our daily life. In spite of designers- effort to develop a web site which be both user-friendly and attractive, it would be difficult to ensure the outcome-s aesthetic quality, since the visual appearance is a matter of an individual self perception and opinion. In this study, it is attempted to develop an automatic system for web pages aesthetic evaluation which are the building blocks of web sites. Based on the image processing techniques and artificial neural networks, the proposed method would be able to categorize the input web page according to its visual appearance and aesthetic quality. The employed features are multiscale/multidirectional textural and perceptual color properties of the web pages, fed to perceptron ANN which has been trained as the evaluator. The method is tested using university web sites and the results suggested that it would perform well in the web page aesthetic evaluation tasks with around 90% correct categorization.

Keywords: Web Page Design, Web Page Aesthetic, Color Spaces, Texture, Neural Networks

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Authors: Mingxin Zhang, Zhaogan Lu, Junyi Shen

Abstract:

In current common research reports, salient regions are usually defined as those regions that could present the main meaningful or semantic contents. However, there are no uniform saliency metrics that could describe the saliency of implicit image regions. Most common metrics take those regions as salient regions, which have many abrupt changes or some unpredictable characteristics. But, this metric will fail to detect those salient useful regions with flat textures. In fact, according to human semantic perceptions, color and texture distinctions are the main characteristics that could distinct different regions. Thus, we present a novel saliency metric coupled with color and texture features, and its corresponding salient region extraction methods. In order to evaluate the corresponding saliency values of implicit regions in one image, three main colors and multi-resolution Gabor features are respectively used for color and texture features. For each region, its saliency value is actually to evaluate the total sum of its Euclidean distances for other regions in the color and texture spaces. A special synthesized image and several practical images with main salient regions are used to evaluate the performance of the proposed saliency metric and other several common metrics, i.e., scale saliency, wavelet transform modulus maxima point density, and important index based metrics. Experiment results verified that the proposed saliency metric could achieve more robust performance than those common saliency metrics.

Keywords: salient regions, color and texture features, image segmentation, saliency metric

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Authors: Akitoshi Uchida, Kazuo Mori

Abstract:

This study examines whether contrived success on a task closely related to school subjects would promote students- self-efficacy. In our previous study, junior high school students who experienced contrived success on anagram tasks raised their sense of self-efficacy and kept it high for a year.We tried to replicate that study, substituting calculation tasks for the anagrams. One hundred eighteen junior high school students participated in this study, 18 of whom were surreptitiously given easier tasks than their classmates. Those students with easier tasks outperformed their peers and thereby raised their sense of self-efficacy. However, elevated self-efficacy did not persist, falling to the starting level after only three months.

Keywords: self-efficacy, contrived success, junior high schoolstudents, calculation tasks

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Authors: B. X. Tchomeni, A. A. Alugongo, T. B. Tengen

Abstract:

In this paper, de Laval rotor system has been characterized by a hinge model and its transient response numerically treated for a dynamic solution. The effect of the ensuing non-linear disturbances namely rub and breathing crack is numerically simulated. Subsequently, three analysis methods: Orbit Analysis, Fast Fourier Transform (FFT), and Wavelet Transform (WT) are employed to extract features of the vibration signal of the faulty system. An analysis of the system response orbits clearly indicates the perturbations due to the rotor-to-stator contact. The sensitivities of WT to the variation in system speed have been investigated by Continuous Wavelet Transform (CWT). The analysis reveals that features of crack, rubs and unbalance in vibration response can be useful for condition monitoring. WT reveals its ability to detect nonlinear signal, and obtained results provide a useful tool method for detecting machinery faults.

Keywords: Continuous wavelet, crack, discrete wavelet, high acceleration, low acceleration, nonlinear, rotor-stator, rub.

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Authors: R. Haddad, K. Morris, V. Buchanan-Wollaston

Abstract:

Biochemical and molecular analysis of some antioxidant enzyme genes revealed different level of gene expression on oilseed (Brassica napus). For molecular and biochemical analysis, leaf tissues were harvested from plants at eight different developmental stages, from young to senescence. The levels of total protein and chlorophyll were increased during maturity stages of plant, while these were decreased during the last stages of plant growth. Structural analysis (nucleotide and deduced amino acid sequence, and phylogenic tree) of a complementary DNA revealed a high level of similarity for a family of Catalase genes. The expression of the gene encoded by different Catalase isoforms was assessed during different plant growth phase. No significant difference between samples was observed, when Catalase activity was statistically analyzed at different developmental stages. EST analysis exhibited different transcripts levels for a number of other relevant antioxidant genes (different isoforms of SOD and glutathione). The high level of transcription of these genes at senescence stages was indicated that these genes are senescenceinduced genes.

Keywords: Biochemical analysis, Oilseed, Expression pattern, Growth phases

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Authors: Kasim Terzic, Bernd Neumann

Abstract:

Scene interpretation systems need to match (often ambiguous) low-level input data to concepts from a high-level ontology. In many domains, these decisions are uncertain and benefit greatly from proper context. This paper demonstrates the use of decision trees for estimating class probabilities for regions described by feature vectors, and shows how context can be introduced in order to improve the matching performance.

Keywords: Classification, Decision Trees, Interpretation, Vision

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Authors: M. Soryani, N. Rafat

Abstract:

Dealing with hundreds of features in character recognition systems is not unusual. This large number of features leads to the increase of computational workload of recognition process. There have been many methods which try to remove unnecessary or redundant features and reduce feature dimensionality. Besides because of the characteristics of Farsi scripts, it-s not possible to apply other languages algorithms to Farsi directly. In this paper some methods for feature subset selection using genetic algorithms are applied on a Farsi optical character recognition (OCR) system. Experimental results show that application of genetic algorithms (GA) to feature subset selection in a Farsi OCR results in lower computational complexity and enhanced recognition rate.

Keywords: Feature Subset Selection, Genetic Algorithms, Optical Character Recognition.

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Authors: T. M. Al Muhareb, J. Graham-Jones

Abstract:

The service quality is a significant element in aviation industry especially in the international airports. Through this paper, the researchers built a model based on Lean six sigma methodologies and applied it in the departure area at KKIA (King Khalid International Airport) in order to assess it. This model characterized with many special features that can become over the cultural differences in aviation industry since it is considered the most critical circumstance in this field. Applying the model of this study is depending on following the DMAIC procedure systemized in lean thinking aspects. This model of Lean-six-sigma as a managerial procedure is mostly focused on the change management culture that requires high level of planning, organizing, modifying, and controlling in order to benefit from strengths as well as revoke weaknesses.

Keywords: Lean-six-sigma, Service quality, Aviation industry, KKIA (King Khalid International Airport), SERVQUAL.

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Authors: T. Hirner, J. Polec

Abstract:

This paper presents an approach for an unequal error protection of facial features of personal ID images coding. We consider unequal error protection (UEP) strategies for the efficient progressive transmission of embedded image codes over noisy channels. This new method is based on the progressive image compression embedded zerotree wavelet (EZW) algorithm and UEP technique with defined region of interest (ROI). In this case is ROI equal facial features within personal ID image. ROI technique is important in applications with different parts of importance. In ROI coding, a chosen ROI is encoded with higher quality than the background (BG). Unequal error protection of image is provided by different coding techniques and encoding LL band separately. In our proposed method, image is divided into two parts (ROI, BG) that consist of more important bytes (MIB) and less important bytes (LIB). The proposed unequal error protection of image transmission has shown to be more appropriate to low bit rate applications, producing better quality output for ROI of the compresses image. The experimental results verify effectiveness of the design. The results of our method demonstrate the comparison of the UEP of image transmission with defined ROI with facial features and the equal error protection (EEP) over additive white gaussian noise (AWGN) channel.

Keywords: Embedded zerotree wavelet (EZW), equal error protection (EEP), facial features, personal ID images, region of interest (ROI), unequal error protection (UEP)

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Authors: Mohamed K. Shahin, Ahmed M. Badawi, Mohamed E. M. Rasmy

Abstract:

Prior research evidenced that unimodal biometric systems have several tradeoffs like noisy data, intra-class variations, restricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates. In order for the biometric system to be more secure and to provide high performance accuracy, more than one form of biometrics are required. Hence, the need arise for multimodal biometrics using combinations of different biometric modalities. This paper introduces a multimodal biometric system (MMBS) based on fusion of whole dorsal hand geometry and fingerprints that acquires right and left (Rt/Lt) near-infra-red (NIR) dorsal hand geometry (HG) shape and (Rt/Lt) index and ring fingerprints (FP). Database of 100 volunteers were acquired using the designed prototype. The acquired images were found to have good quality for all features and patterns extraction to all modalities. HG features based on the hand shape anatomical landmarks were extracted. Robust and fast algorithms for FP minutia points feature extraction and matching were used. Feature vectors that belong to similar biometric traits were fused using feature fusion methodologies. Scores obtained from different biometric trait matchers were fused using the Min-Max transformation-based score fusion technique. Final normalized scores were merged using the sum of scores method to obtain a single decision about the personal identity based on multiple independent sources. High individuality of the fused traits and user acceptability of the designed system along with its experimental high performance biometric measures showed that this MMBS can be considered for med-high security levels biometric identification purposes.

Keywords: Unimodal, Multi-Modal, Biometric System, NIR Imaging, Dorsal Hand Geometry, Fingerprint, Whole Hands, Feature Extraction, Feature Fusion, Score Fusion

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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: Khalid Salhi, El Miloud Jaara, Mohammed Talibi Alaoui

Abstract:

In this paper, we present a neural approach for unsupervised natural color-texture image segmentation, which is based on both Kohonen maps and mathematical morphology, using a combination of the texture and the image color information of the image, namely, the fractal features based on fractal dimension are selected to present the information texture, and the color features presented in RGB color space. These features are then used to train the network Kohonen, which will be represented by the underlying probability density function, the segmentation of this map is made by morphological watershed transformation. The performance of our color-texture segmentation approach is compared first, to color-based methods or texture-based methods only, and then to k-means method.

Keywords: Segmentation, color-texture, neural networks, fractal, watershed.

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Authors: Jia-Ru Li, Chih-Hung Wang, Ching-Wen Lin

Abstract:

The association between emotional inhibition strategies linked to depression has been showed inconsistent among studies. Mild emotional inhibition maybe benefit for social interaction, especially for female among East Asian cultures. The present study aimed to examine whether the inhibition–depression relationship is dependent on level of emotion inhibition and gender context, given differing value of suppressing emotional displays. We hypothesized that the negative associations between inhibition and adolescent depression would not directly, in which affected by interaction between emotion inhibition and gender. To test this hypothesis, we asked 309 junior high school students which age range from 12 to14 years old to report on their use of emotion inhibition and depression syndrome. A multiple regressions analysis revealed that significant interaction that gender as a moderator to the relationships between emotion inhibition and adolescent depression. The group with the highest level of depression was girls with high levels of emotion inhibition, whose depression score was higher than that of boys with high levels of emotion inhibition. The result highlights that the importance of context in understanding the inhibition-depression relationship.

Keywords: Emotional inhibition strategies, gender, adolescent depression.

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