Search results for: Human Body Recognition

Authors: Koray U. Erbas

Abstract:

Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: Convolutional Neural Network, Deep Learning, Deep Learning Based FER, Facial Emotion Recognition.

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Authors: Carlos Vera, Tetyana Baydyk, Ernst Kussul, Graciela Velasco, Miguel Aparicio

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This article describes the Random Subspace Neural Classifier (RSC) for the recognition of meteors in the night sky. We used images of meteors entering the atmosphere at night between 8:00 p.m.-5: 00 a.m. The objective of this project is to classify meteor and star images (with stars as the image background). The monitoring of the sky and the classification of meteors are made for future applications by scientists. The image database was collected from different websites. We worked with RGB-type images with dimensions of 220x220 pixels stored in the BitMap Protocol (BMP) format. Subsequent window scanning and processing were carried out for each image. The scan window where the characteristics were extracted had the size of 20x20 pixels with a scanning step size of 10 pixels. Brightness, contrast and contour orientation histograms were used as inputs for the RSC. The RSC worked with two classes and classified into: 1) with meteors and 2) without meteors. Different tests were carried out by varying the number of training cycles and the number of images for training and recognition. The percentage error for the neural classifier was calculated. The results show a good RSC classifier response with 89% correct recognition. The results of these experiments are presented and discussed.

Keywords: Contour orientation histogram, meteors, night sky, RSC neural classifier, stars.

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Authors: Ksheeraj Sai Vepuri, Nada Attar

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We as humans use words with accompanying visual and facial cues to communicate effectively. Classifying facial emotion using computer vision methodologies has been an active research area in the computer vision field. In this paper, we propose a simple method for facial expression recognition that enhances accuracy. We tested our method on the FER-2013 dataset that contains static images. Instead of using Histogram equalization to preprocess the dataset, we used Unsharp Mask to emphasize texture and details and sharpened the edges. We also used ImageDataGenerator from Keras library for data augmentation. Then we used Convolutional Neural Networks (CNN) model to classify the images into 7 different facial expressions, yielding an accuracy of 69.46% on the test set. Our results show that using image preprocessing such as the sharpening technique for a CNN model can improve the performance, even when the CNN model is relatively simple.

Keywords: Facial expression recognition, image pre-processing, deep learning, CNN.

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Authors: Yi-an Cui, Lu Wang, Jian-ping Xiao

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This paper presents an automatic feature recognition method based on center-surround difference detecting and fuzzy logic that can be applied in ground-penetrating radar (GPR) image processing. Adopted center-surround difference method, the salient local image regions are extracted from the GPR images as features of detected objects. And fuzzy logic strategy is used to match the detected features and features in template database. This way, the problem of objects detecting, which is the key problem in GPR image processing, can be converted into two steps, feature extracting and matching. The contributions of these skills make the system have the ability to deal with changes in scale, antenna and noises. The results of experiments also prove that the system has higher ratio of features sensing in using GPR to image the subsurface structures.

Keywords: feature recognition, GPR image, matching strategy, salient image

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Authors: H. H. Huynh, J. Meunier, J.Sequeira, M.Daniel

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In this paper, the detection and tracking of face, mouth, hands and medication bottles in the context of medication intake monitoring with a camera is presented. This is aimed at recognizing medication intake for elderly in their home setting to avoid an inappropriate use. Background subtraction is used to isolate moving objects, and then, skin and bottle segmentations are done in the RGB normalized color space. We use a minimum displacement distance criterion to track skin color regions and the R/G ratio to detect the mouth. The color-labeled medication bottles are simply tracked based on the color space distance to their mean color vector. For the recognition of medication intake, we propose a three-level hierarchal approach, which uses activity-patterns to recognize the normal medication intake activity. The proposed method was tested with three persons, with different medication intake scenarios, and gave an overall precision of over 98%.

Keywords: Activity recognition, background subtraction, tracking, medication intake, video surveillance

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Authors: Asma Rabaoui, Zied Lachiri, Noureddine Ellouze

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Discrimination between different classes of environmental sounds is the goal of our work. The use of a sound recognition system can offer concrete potentialities for surveillance and security applications. The first paper contribution to this research field is represented by a thorough investigation of the applicability of state-of-the-art audio features in the domain of environmental sound recognition. Additionally, a set of novel features obtained by combining the basic parameters is introduced. The quality of the features investigated is evaluated by a HMM-based classifier to which a great interest was done. In fact, we propose to use a Multi-Style training system based on HMMs: one recognizer is trained on a database including different levels of background noises and is used as a universal recognizer for every environment. In order to enhance the system robustness by reducing the environmental variability, we explore different adaptation algorithms including Maximum Likelihood Linear Regression (MLLR), Maximum A Posteriori (MAP) and the MAP/MLLR algorithm that combines MAP and MLLR. Experimental evaluation shows that a rather good recognition rate can be reached, even under important noise degradation conditions when the system is fed by the convenient set of features.

Keywords: Sounds recognition, HMM classifier, Multi-style training, Environmental Adaptation, Feature combinations.

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Authors: Marlene Weber, Joseph Giacomin, Alessio Malizia, Lee Skrypchuk, Voula Gkatzidou

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Technological and sociological developments in the automotive sector are shifting the focus of design towards developing a better understanding of driver needs, desires and emotions. Human centred design methods are being more frequently applied to automotive research, including the use of systems to detect human emotions in real-time. One method for a non-contact measurement of emotion with low intrusiveness is Facial-Expression Analysis (FEA). This paper describes a research study investigating emotional responses of 22 participants in a naturalistic driving environment by applying a multi-method approach. The research explored the possibility to investigate emotional responses and their frequencies during naturalistic driving through real-time FEA. Observational analysis was conducted to assign causes to the collected emotional responses. In total, 730 emotional responses were measured in the collective study time of 440 minutes. Causes were assigned to 92% of the measured emotional responses. This research establishes and validates a methodology for the study of emotions and their causes in the driving environment through which systems and factors causing positive and negative emotional effects can be identified.

Keywords: Affective computing, case study, emotion recognition, human computer interaction.

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Authors: Panagiotis Pentaris

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The issue of social and legal recognition of LGBTQ families is of high importance when exploring the possibility of a family. Of equal importance is the fact that both society and the individual contribute to the overall recognition of LGBTQ families. This paper is a conceptual discussion, by methodology, of both sides; it uses a method of constructive analysis to expound on this issue. This method’s aim is to broaden conceptual theory, and introduce a new relationship between concepts that were previously not associated by evidence. This exploration has found that LGBTQ realities from an international perspective may differ and both legal and social rights are critical toward self-consciousness and the formation of a family. This paper asserts that internalised and historic oppression of LGBTQ individuals, places them, not always and not in all places, in a disadvantageous position as far as engaging with the potential of forming a family goes. The paper concludes that lack of social recognition and internalised oppression are key barriers regarding LGBTQ families.

Keywords: Family, gay, LGBTQ, self-worth, social rights.

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Authors: Jun Su Ha

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Various advanced technologies will be adopted in Advanced Control Rooms (ACRs) of advanced Nuclear Power Plants (NPPs), which is thought to increase operators’ performance. However, potential human factors issues coupled with digital technologies might be troublesome. Human factors issues in ACRs are identified and strategies (or countermeasures) for evaluating and analyzing each of issues are addressed in this study.

 

Keywords: Advanced control room, human factor issues, human performance, human error, nuclear power plant.

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Authors: Bu Jianhui, Bi Jinshun, Liu Mengxin, Luo Jiajun, Han Zhengsheng

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The floating body effect is a serious problem for the PDSOI MOSFET, and the H-gate layout is frequently used as the body contact to eliminate this effect. Unfortunately, most of the standard commercial SOI MOSFET model is for the device with finger gate, the necessity of the new models for the H-gate device arises. A simulation model for the H-gate PDSOI MOSFET is proposed based on the 0.35μm PDSOI process developed by the Institute of Microelectronics of the Chinese Academy of Sciences (IMECAS), and then the model is well verified by the ring-oscillator.

Keywords: PDSOI H-gate Device model Body contact.

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Authors: Vladyslav Povoroznyuk, Anna Musiienko, Nataliia Dzerovych, Roksolana Povoroznyuk, Oksana Ivanyk

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Deficiency and insufficiency of Vitamin D is a pandemic of the 21^st century. Obesity patients have a lower level of vitamin D, but the literature data are contradictory. The purpose of this study is to investigate deficiency and insufficiency vitamin D in postmenopausal women with obesity. We examined 1007 women aged 50-89 years. Mean age was 65.74±8.61 years; mean height was 1.61±0.07 m; mean weight was 70.65±13.50 kg; mean body mass index was 27.27±4.86 kg/m², and mean 25(OH) D levels in serum was 26.00±12.00 nmol/l. The women were divided into the following six groups depending on body mass index: I group – 338 women with normal body weight, II group – 16 women with insufficient body weight, III group – 382 women with excessive body weight, IV group – 199 women with obesity of class I, V group – 60 women with obesity of class II, and VI group – 12 women with obesity of class III. Level of 25(OH)D in serum was measured by means of an electrochemiluminescent method - Elecsys 2010 analyzer (Roche Diagnostics, Germany) and cobas test-systems. 34.4% of the examined women have deficiency of vitamin D and 31.4% insufficiency. Women with obesity of class I (23.60±10.24 ng/ml) and obese of class II (22.38±10.34 ng/ml) had significantly lower levels of 25 (OH) D compared to women with normal body weight (28.24±12.99 ng/ml), p=0.00003. In women with obesity, BMI significantly influences vitamin D level, and this influence does not depend on the season.

Keywords: Obesity, body mass index, vitamin D deficiency/insufficiency, postmenopausal women, age.

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Authors: Zh. D. Dadebayev, M.T. Kozhakanova, I.K.Azimbayeva

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The issue of human anthropology took an important role in the last epochs and still hasn-t lost its importance. Scientists of different countries were interested in investigating the appearance of human being and the idea of life after death. While writing this article we noticed that scientists who made research in this issue, despite of the different countries and different epochs in which they lived, had similarities in their opinions. In given article we wrote great Kazakh poet AbaiKunanbayev-s philosophical view to the problem of human anthropology.

Keywords: anthropology, concept, human, nature

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Authors: Zurriati Mohd Ali, Wahyu Kuntjoro, Wirachman Wisnoe

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The aerodynamics characteristics of a blended-wing body (BWB) aircraft were obtained in Universiti Teknologi MARA low speed wind tunnel. The scaled-down of BWB model consisted of a canard as its horizontal stabilizer. There were four canards with different aspect ratio used in the experiments. Canard setting angles were varied from -20q to 20q. All tests were conducted at velocity of 35 m/s, with Mach number 0.1. At low angles of attacks, the increment of lift slope for various canards aspect ratio is small and almost constant. Higher canard aspect ratio will cause higher drag. However, canard has a high effect to the moment at zero lift, CM,0.The visualization using mini tuff was performed to observe the airflow at the upper surface of canard. KeywordsAerodynamics,blended-wing body, canard, wind tunnel.

Keywords: Aerodynamics, blended-wing body, canard, wind tunnel.

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Authors: Marija Lončar, Zorana Šuljug Vučica, Magdalena Nigoević

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Diverse social, cultural and economic trends and changes in contemporary societies influence the ways masculinity is represented in a variety of media. Masculinity is constructed within media images as a dynamic process that changes slowly over time and is shaped by various social factors. In many societies, dominant masculinity is still associated with authority, heterosexuality, marriage, professional and financial success, ethnic dominance and physical strength. But contemporary media depict men in ways that suggest a change in the approach to media images. The number of media images of men, which promote men’s identity through their body, have increased. With the male body more scrutinized and commodified, it is necessary to highlight how the body is represented and which visual elements are crucial since the body has an important role in the construction of masculinities. The study includes content analysis of male body images in the advertisements of different men’s and women’s lifestyle magazines available in Croatia. The main aim was to explore how masculinities are currently being portrayed through body regarding age, physical appearance, fashion, touch and gaze. The findings are also discussed in relation to female images since women are central in many of the processes constructing masculinities and according to the recent conceptualization of masculinity. Although the construction of male images varies through body features, almost all of them convey the message that men’s identity could be managed through manipulation and by enhancing the appearance. Furthermore, they suggest that men should engage in “bodywork” through advertised products, activities and/or practices, in order to achieve their preferred social image.

Keywords: Body images, content analysis, lifestyle magazines, masculinity.

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Authors: Khenfer-Koummich Fatima, Mesbahi Larbi, Hendel Fatiha

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This paper presents a recognition system for isolated words like robot commands. It’s carried out by Time Delay Neural Networks; TDNN. To teleoperate a robot for specific tasks as turn, close, etc… In industrial environment and taking into account the noise coming from the machine. The choice of TDNN is based on its generalization in terms of accuracy, in more it acts as a filter that allows the passage of certain desirable frequency characteristics of speech; the goal is to determine the parameters of this filter for making an adaptable system to the variability of speech signal and to noise especially, for this the back propagation technique was used in learning phase. The approach was applied on commands pronounced in two languages separately: The French and Arabic. The results for two test bases of 300 spoken words for each one are 87%, 97.6% in neutral environment and 77.67%, 92.67% when the white Gaussian noisy was added with a SNR of 35 dB.

Keywords: Neural networks, Noise, Speech Recognition.

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Authors: D. A. K. S. Gunaratna, N. D. Kodikara, H. L. Premaratne

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Automatic currency note recognition invariably depends on the currency note characteristics of a particular country and the extraction of features directly affects the recognition ability. Sri Lanka has not been involved in any kind of research or implementation of this kind. The proposed system “SLCRec" comes up with a solution focusing on minimizing false rejection of notes. Sri Lankan currency notes undergo severe changes in image quality in usage. Hence a special linear transformation function is adapted to wipe out noise patterns from backgrounds without affecting the notes- characteristic images and re-appear images of interest. The transformation maps the original gray scale range into a smaller range of 0 to 125. Applying Edge detection after the transformation provided better robustness for noise and fair representation of edges for new and old damaged notes. A three layer back propagation neural network is presented with the number of edges detected in row order of the notes and classification is accepted in four classes of interest which are 100, 500, 1000 and 2000 rupee notes. The experiments showed good classification results and proved that the proposed methodology has the capability of separating classes properly in varying image conditions.

Keywords: Artificial intelligence, linear transformation and pattern recognition.

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Authors: M A Hannan, A. Hussain, S. A. Samad, K. A. Ishak, A. Mohamed

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This paper presents a general trainable framework for fast and robust upright human face and non-human object detection and verification in static images. To enhance the performance of the detection process, the technique we develop is based on the combination of fast neural network (FNN) and classical neural network (CNN). In FNN, a useful correlation is exploited to sustain high level of detection accuracy between input image and the weight of the hidden neurons. This is to enable the use of Fourier transform that significantly speed up the time detection. The combination of CNN is responsible to verify the face region. A bootstrap algorithm is used to collect non human object, which adds the false detection to the training process of the human and non-human object. Experimental results on test images with both simple and complex background demonstrate that the proposed method has obtained high detection rate and low false positive rate in detecting both human face and non-human object.

Keywords: Algorithm, detection of human and non-human object, FNN, CNN, Image training.

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Authors: Y. Kurosawa, T. Yamaguchi

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A numerical method is proposed to calculate damping properties for sound-proof structures involving elastic body, viscoelastic body, and porous media. For elastic and viscoelastic body displacement is modeled using conventional finite elements including complex modulus of elasticity. Both effective density and bulk modulus have complex quantities to represent damped sound fields in the porous media. Particle displacement in the porous media is discretised using finite element method. Displacement vectors as common unknown variables are solved under coupled condition between elastic body, viscoelastic body and porous media. Further, explicit expressions of modal loss factor for the mixed structures are derived using asymptotic method. Eigenvalue analysis and frequency responded were calculated for automotive test panel laminated viscoelastic and porous structures using this technique, the results almost agreed with the experimental results.

Keywords: Damping, Porous Media, Finite Element Method, Computer Aided Engineering.

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Authors: Hossein Esbati, Jalil Shirazi

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In this paper, in order to categorize ORL database face pictures, principle Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) methods by using Elman neural network and Support Vector Machine (SVM) categorization methods are used. Elman network as a recurrent neural network is proposed for modeling storage systems and also it is used for reviewing the effect of using PCA numbers on system categorization precision rate and database pictures categorization time. Categorization stages are conducted with various components numbers and the obtained results of both Elman neural network categorization and support vector machine are compared. In optimum manner 97.41% recognition accuracy is obtained.

Keywords: Face recognition, Principal Component Analysis, Kernel Principal Component Analysis, Neural network, Support Vector Machine.

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Authors: M. Debyeche, J.P Haton, A. Houacine

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The paper presents a complete discrete statistical framework, based on a novel vector quantization (VQ) front-end process. This new VQ approach performs an optimal distribution of VQ codebook components on HMM states. This technique that we named the distributed vector quantization (DVQ) of hidden Markov models, succeeds in unifying acoustic micro-structure and phonetic macro-structure, when the estimation of HMM parameters is performed. The DVQ technique is implemented through two variants. The first variant uses the K-means algorithm (K-means- DVQ) to optimize the VQ, while the second variant exploits the benefits of the classification behavior of neural networks (NN-DVQ) for the same purpose. The proposed variants are compared with the HMM-based baseline system by experiments of specific Arabic consonants recognition. The results show that the distributed vector quantization technique increase the performance of the discrete HMM system.

Keywords: Hidden Markov Model, Vector Quantization, Neural Network, Speech Recognition, Arabic Language

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Authors: S. Parasuraman, Ler Shiaw Pei

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In this paper, the bio-mechanical analysis of human joints is carried out and the study is extended to the robot manipulator. This study will first focus on the kinematics of human arm which include the movement of each joint in shoulder, wrist, elbow and finger complexes. Those analyses are then extended to the design of a human robot manipulator. A simulator is built for Direct Kinematics and Inverse Kinematics of human arm. In the simulation of Direct Kinematics, the human joint angles can be inserted, while the position and orientation of each finger tips (end-effector) are shown. Inverse Kinematics does the reverse of the Direct Kinematics. Based on previous materials obtained from kinematics analysis, the human manipulator joints can be designed to follow prescribed position trajectories.

Keywords: Kinematics, Human Joints, Robotics, Robot Dynamics, Manipulators.

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Authors: Ahmed Abdulghani Taha, Mohammad Abdulghani Taha

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This study aims at being acquainted with the using the body fat percentage (%BF) with body Mass Index (BMI) as input parameters in fuzzy logic decision support system to predict properly the lifted weight for students at weightlifting class lift according to his abilities instead of traditional manner. The sample included 53 male students (age = 21.38 ± 0.71 yrs, height (Hgt) = 173.17 ± 5.28 cm, body weight (BW) = 70.34 ± 7.87.6 kg, Body mass index (BMI) 23.42 ± 2.06 kg.m-2, fat mass (FM) = 9.96 ± 3.15 kg and fat percentage (% BF) = 13.98 ± 3.51 %.) experienced the weightlifting class as a credit and has variance at BW, Hgt and BMI and FM. BMI and % BF were taken as input parameters in FUZZY logic whereas the output parameter was the lifted weight (LW). There were statistical differences between LW values before and after using fuzzy logic (Diff 3.55± 2.21, P > 0.001). The percentages of the LW categories proposed by fuzzy logic were 3.77% of students to lift 1.0 fold of their bodies; 50.94% of students to lift 0.95 fold of their bodies; 33.96% of students to lift 0.9 fold of their bodies; 3.77% of students to lift 0.85 fold of their bodies and 7.55% of students to lift 0.8 fold of their bodies. The study concluded that the characteristic changes in body composition experienced by students when undergoing weightlifting could be utilized side by side with the Fuzzy logic decision support system to determine the proper workloads consistent with the abilities of students.

Keywords: Fuzzy logic, body mass index, body fat percentage, weightlifting.

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Authors: Muhammad Tayyab, Saba Saira

Abstract:

It is an established fact that organizations across the globe consider employees as their assets and try to advance their well-being. However, the local firms of developing countries are mostly profit oriented and do not have much concern about their employees’ engagement or commitment. Like other developing countries, the local organizations of Pakistan are also less concerned about the well-being of their employees. Especially public sector organizations lack concern regarding engagement, satisfaction or commitment of the employees. Therefore, this study aimed at investigating the impact of job recognition and job motivation on organizational commitment in the mediation role of employee engagement. The data were collected from land record officers of board of revenue, Punjab, Pakistan. Structured questionnaire was used to collect data through physically visiting land record officers and also through the internet. A total of 318 land record officers’ responses were finalized to perform data analysis. The data were analyzed through confirmatory factor analysis and structural equation modeling technique. The findings revealed that job recognition and job motivation have direct as well as indirect positive and significant impact on organizational commitment. The limitations, practical implications and future research indications are also explained.

Keywords: Job motivation, job recognition, employee engagement, employee commitment, public sector, land record officers.

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Authors: Ghazy M.R. Assassa, Mona F. M. Mursi, Hatim A. Aboalsamh

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Traditional principal components analysis (PCA) techniques for face recognition are based on batch-mode training using a pre-available image set. Real world applications require that the training set be dynamic of evolving nature where within the framework of continuous learning, new training images are continuously added to the original set; this would trigger a costly continuous re-computation of the eigen space representation via repeating an entire batch-based training that includes the old and new images. Incremental PCA methods allow adding new images and updating the PCA representation. In this paper, two incremental PCA approaches, CCIPCA and IPCA, are examined and compared. Besides, different learning and testing strategies are proposed and applied to the two algorithms. The results suggest that batch PCA is inferior to both incremental approaches, and that all CCIPCAs are practically equivalent.

Keywords: Candid covariance-free incremental principal components analysis (CCIPCA), face recognition, incremental principal components analysis (IPCA).

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Authors: Gulten Yilmaz, A. Beril Tugrul, Mustafa Demir, Dogan Yasar, Bayram Demir, Bulent Buyuk

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A Positron Emission Tomography (PET) is a radioisotope imaging technique that illustrates the organs and the metabolisms of the human body. This technique is based on the simultaneous detection of 511 keV annihilation photons, annihilated as a result of electrons annihilating positrons that radiate from positron-emitting radioisotopes that enter biological active molecules in the body. This study was conducted on ten patients in an effort to conduct patient-related experimental studies. Dosage monitoring for the bladder, which was the organ that received the highest dose during PET applications, was conducted for 24 hours. Assessment based on measuring urination activities after injecting patients was also a part of this study. The MIRD method was used to conduct dosage calculations for results obtained from experimental studies. Results obtained experimentally and theoretically were assessed comparatively.

Keywords: PET/CT, TLD, MIRD, Dose measurement, Patient doses.

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Authors: J.-C. Cexus, A.O. Boudraa

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In this paper, a new approach for target recognition based on the Empirical mode decomposition (EMD) algorithm of Huang etal. [11] and the energy tracking operator of Teager [13]-[14] is introduced. The conjunction of these two methods is called Teager-Huang analysis. This approach is well suited for nonstationary signals analysis. The impulse response (IR) of target is first band pass filtered into subsignals (components) called Intrinsic mode functions (IMFs) with well defined Instantaneous frequency (IF) and Instantaneous amplitude (IA). Each IMF is a zero-mean AM-FM component. In second step, the energy of each IMF is tracked using the Teager energy operator (TEO). IF and IA, useful to describe the time-varying characteristics of the signal, are estimated using the Energy separation algorithm (ESA) algorithm of Maragos et al .[16]-[17]. In third step, a set of features such as skewness and kurtosis are extracted from the IF, IA and IMF energy functions. The Teager-Huang analysis is tested on set of synthetic IRs of Sonar targets with different physical characteristics (density, velocity, shape,? ). PCA is first applied to features to discriminate between manufactured and natural targets. The manufactured patterns are classified into spheres and cylinders. One hundred percent of correct recognition is achieved with twenty three echoes where sixteen IRs, used for training, are free noise and seven IRs, used for testing phase, are corrupted with white Gaussian noise.

Keywords: Target recognition, Empirical mode decomposition, Teager-Kaiser energy operator, Features extraction.

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Authors: Y. Obikane, T. Nemoto , K. Ogura, M. Iwata, K. Ono

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The objective of this research was to find the diffusion properties of vehicles on the road by using the V-Sphere Code. The diffusion coefficient and the size of the height of the wake were estimated with the LES option and the third order MUSCL scheme. We evaluated the code with the changes in the moments of Reynolds Stress along the mean streamline. The results show that at the leading part of a bluff body the LES has some advantages over the RNS since the changes in the strain rates are larger for the leading part. We estimated that the diffusion coefficient with the computed Reynolds stress (non-dimensional) was about 0.96 times the mean velocity.

Keywords: Wake , bluff body, V-CAD, turbulence diffusion.

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Authors: A. J. Sanda, O. Olowofeso, M. A. Adeleke, A. O Oso, S. O. Durosaro, M. O. Sanda

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A total of 150 meat type chickens comprising 50 each of Arbor Acre, Marshall and Ross were used for this study which lasted for 10 weeks at the Federal University of Agriculture, Abeokuta, Nigeria. Growth performance data were collected from the third week through week 10 and data obtained were analysed using the Generalized Linear Model Procedure. Heritability estimates (h2) for body dimensions carried out on the chicken strains ranged from low to high. Marshall broiler chicken strain had the highest h2 for body weight 0.46±0.04, followed by Arbor Acre and Ross with h2 being 0.38±0.12 and 0.26±0.06, respectively. The repeatability estimates for body weight in the three broiler strains were high, and it ranged from 0.70 at week 4 to 0.88 at week 10. Relationships between the body weight and linear body measurements in the broiler chicken strains were positive and highly significant (p > 0.05).

Keywords: Broiler chicken strains, heritability, repeatability, traits.

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Authors: Yucel Ugurlu

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This paper explains a novel approach to human interactive e-learning systems using head posture images. Students- face and hair information are used to identify a human presence and estimate the gaze direction. We then define the human-computer interaction level and test the definition using ten students and seventy different posture images. The experimental results show that head posture images provide adequate information for increasing human-computer interaction in e-learning systems.

Keywords: E-learning, image segmentation, human-presence, gaze-direction, human-computer interaction, LabVIEW

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Authors: Luciano Nieddu, Giuseppe Manfredi

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In this paper a Pattern Recognition algorithm based on a constrained version of the k-means clustering algorithm will be presented. The proposed algorithm is a non parametric supervised statistical pattern recognition algorithm, i.e. it works under very mild assumptions on the dataset. The performance of the algorithm will be tested, togheter with a feature extraction technique that captures the information on the closed two-dimensional contour of an image, on images of industrial mineral ores.

Keywords: K-means, Industrial ores classification, Invariant Features, Supervised Classification.

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