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Technological Developments in Networking, Education and Automation

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Technological Developments in Networking, Education and Automation includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the following areas: Computer Networks: Access Technologies, Medium Access Control, Network architectures and Equipment, Optical Networks and Switching, Telecommunication Technology, and Ultra Wideband Communications. Engineering Education and Online Learning: including development of courses and systems for engineering, technical and liberal studies programs; online laboratories; intelligent testing using fuzzy logic; taxonomy of e-courses; and evaluation of online courses. Pedagogy: including benchmarking; group-learning; active learning; teaching of multiple subjects together; ontology; and knowledge management. Instruction Technology: including internet textbooks; virtual reality labs, instructional design, virtual models, pedagogy-oriented markup languages; graphic design possibilities; open source classroom management software; automatic email response systems; tablet-pcs; personalization using web mining technology; intelligent digital chalkboards; virtual room concepts for cooperative scientific work; and network technologies, management, and architecture. Coding and Modulation: Modeling and Simulation, OFDM technology , Space-time Coding, Spread Spectrum and CDMA Systems. Wireless technologies: Bluetooth , Cellular Wireless Networks, Cordless Systems and Wireless Local Loop, HIPERLAN, IEEE 802.11, Mobile Network Layer, Mobile Transport Layer, and Spread Spectrum. Network Security and applications: Authentication Applications, Block Ciphers Design Principles, Block Ciphers Modes of Operation, Electronic Mail Security, Encryption & Message Confidentiality, Firewalls, IP Security, Key Cryptography & Message Authentication, and Web Security. Robotics, Control Systems and Automation: Distributed Control Systems, Automation, Expert Systems, Robotics, Factory Automation, Intelligent Control Systems, Man Machine Interaction, Manufacturing Information System, Motion Control, and Process Automation. Vision Systems: for human action sensing, face recognition, and image processing algorithms for smoothing of high speed motion. Electronics and Power Systems: Actuators, Electro-Mechanical Systems, High Frequency Converters, Industrial Electronics, Motors and Drives, Power Converters, Power Devices and Components, and Power Electronics.
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K. Elleithy, University of Bridgeport, Bridgeport, USA; T. Sobh, University of Bridgeport,
Bridgeport, USA; M. Iskander, Polytechnic University, Brooklyn, USA; V. Kapila,
Polytechnic University, Brooklyn, USA; M.A. Karim, Old Dominion University, Norfolk, USA;
A. Mahmood, University of Bridgeport, Bridgeport, USA (Eds.)
Technological Developments in Networking, Education and
Automation
Collects papers presented at a high-caliber research conference
All manuscripts have passed at least two reviews
Covers a multidisciplinary range of topics, from an international
perspective
Technological Developments in Networking, Education and Automation includes a set
of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art
research projects in the following areas:
Computer Networks: Access Technologies, Medium Access Control, Network architectures
and Equipment, Optical Networks and Switching, Telecommunication Technology, and
Ultra Wideband Communications.
Engineering Education and Online Learning: including development of courses and
systems for engineering, technical and liberal studies programs; online laboratories;
intelligent testing using fuzzy logic; taxonomy of e-courses; and evaluation of online
courses.
Pedagogy: including benchmarking; group-learning; active learning; teaching of multiple
subjects together; ontology; and knowledge management.
Instruction Technology: including internet textbooks; virtual reality labs, instructional
design, virtual models, pedagogy-oriented markup languages; graphic design
possibilities; open source classroom management software; automatic email response
systems; tablet-pcs; personalization using web mining technology; intelligent digital
chalkboards; virtual room concepts for cooperative scientific work; and network
technologies, management, and architecture.
Coding and Modulation: Modeling and Simulation, OFDM technology , Space-time
Coding, Spread Spectrum and CDMA Systems.
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... In such designs, the learning experienced by students differs from that in solely face-to-face contexts, as the course activities require students to constantly move back and forth between physical and virtual spaces and to actively seek to understand how different components are related to and complementary with each other . This not only creates new ways of conceptualising but also requires new ways of assessing student learning experience (Iskander et al., 2010). ...
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This study combined the methods from student approaches to learning and learning analytics research by using both self-reported and observational measures to examine the student learning experience. It investigated the extent to which reported approaches and perceptions and observed online interactions are related to each other and how they contribute to variation in academic performance in a blended course design. Correlation analyses showed significant pairwise associations between approaches and frequency of the online interaction. A cluster analysis identified two groupings of students with different reported learning orientations. Based on the reported learning orientations, one-way ANOVAs showed that students with understanding orientation reported deep approaches to and positive perceptions of learning. The students with understanding orientation also interacted more frequently with the online learning tasks and had higher marks than those with reproducing orientation, who reported surface approaches and negative perceptions. Regression analyses found that adding the observational measures increased 36% of the variance in the academic performance in comparison with using self-reported measures alone (6%). The findings suggest using the combined methods to explain students’ academic performance in blended course designs not only triangulates the results but also strengthens the acuity of the analysis. © 2020. Articles published in the Australasian Journal of Educational Technology (AJET) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BYNC- ND 4.0). Authors retain copyright in their work and grant AJET right of first publication under CC BY-NC-ND 4.0. All Rights Reserved.
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Chapter
Industrial Cyber-Physical Systems require appropriate security mechanisms to provide protection against cyber attackers. In this paper, we propose a security architecture for a gateway connecting production and cloud systems. A Trusted Platform Module 2.0 is used for protecting the cryptographic keys used in secure communication protocols and to provide protection against illegitimate firmware manipulation. As proof of concept, we implemented the key protection functionality with a TPM 2.0 for the OPC UA protocol.
Chapter
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Thesis
Among all cancers, lung cancer (LC) is one of the most common tumors, an increase of 2% per year on its worldwide incidence. In Brazil, for the year of 2014, 27,330 new cases of LC are estimated, these being 16,400 in men and 10,930 in women. In this context, it is of fundamental importance for public health the identification on early stages of lung diseases. The diagnosis assistance shows to be important both from a clinical standpoint as in research. Among the factors contributing to this scene, one important is the increasing accuracy of diagnosis of a medical expert as you increase the number of information about the patient's condition. Thus, certain disorders might be detected early, including saving lives in some cases. The initial treatment for this disease consists of lobectomy. In this context, it is customary to perform the segmentation of lung lobes in CT images to extract data and assist in planning for lobectomy. The segmentation of the lobes from CT images is usually obtained by detection of pulmonary fissures. Thus, in order to obtain a more effective segmentation of pulmonary fissures, and perform a completely independent process from the other structures present in the CT scan, the present work has the objective to perform the fissure segmentation using LBP texture measures and Neural Networks (NN). To implement the algorithm we used one MLP with 60 inputs, 120 hidden neurons and 2 output neurons. The input parameters for the network was the LBP histogram of the voxel being analyzed. For network training, it was necessary to create a system to label the features as fissures and non-fissures manually, where the user selects the fissure pixels class. To perform the validation of the algorithm was necessary to create a "gold standard" in which it was extracted a total of 100 images from 5 exams from the dataset LOLA11, where these images were the fissures were highlighted by two experts. From the gold standard, the proposed algorithm was processed and the results were obtained. For all tested images, the classifier obtained a better performance when the size of 15x15 pixels of the window was used to generate the histogram of the LBP. To get to this definition were tested sizes of 11x11, 15x15, 17x17 and 21x21 and the results were based on metrics ACC(%), TPR(%), SPC(%) distance mean and standard deviation of the distance. The first approach to analyze the results is through the voxels defined as fissure at the end of the proposed methodology. For the proposed methodology, using automatic detection and MLP LBP before thinning, the rates were obtained ACC = 96.7 %, TPR = 69.6 % and SPC = 96.8 % and ACC = 99 2 % TPR = 3 % and SPC = 99.81 % for the proposed method with the thinning in the end, considering the incidence of false positives and false negatives. Another approach used in the literature for evaluating methods of fissure segmentation is based on the average distance between the fissure delineated by the expert and the resulting fissure through the algorithm. Thus, the algorithm proposed in this paper was compared with the algorithm Lassen(2013) by the average distance between the manual segmented and the automatically segmented fissure. The proposed algorithm with the thinning in the end achieved a shorter distance average value and a lower standard deviation compared with the method of Lassen(2013). Finally, the results obtained for automatic segmentation of lung fissures are presented. The low incidence of false negative detections detection results, together with the significant reduction in false positive detections result in a high rate of settlement. We conclude that the segmentation technique for lung fissures is a useful target for pulmonary fissures on CT images and has potential to integrate systems that help medical diagnosis.
Full-text available
Thesis
Among all cancers, lung cancer (LC) is one of the most common tumors, an increase of 2% per year on its worldwide incidence. In Brazil, for the year of 2014, 27,330 new cases of LC are estimated, these being 16,400 in men and 10,930 in women. In this context, it is of fundamental importance for public health the identification on early stages of lung diseases. The diagnosis assistance shows to be important both from a clinical standpoint as in research. Among the factors contributing to this scene, one important is the increasing accuracy of diagnosis of a medical expert as you increase the number of information about the patient's condition. Thus, certain disorders might be detected early, including saving lives in some cases. The initial treatment for this disease consists of lobectomy. In this context, it is customary to perform the segmentation of lung lobes in CT images to extract data and assist in planning for lobectomy. The segmentation of the lobes from CT images is usually obtained by detection of pulmonary fissures. Thus, in order to obtain a more effective segmentation of pulmonary fissures, and perform a completely independent process from the other structures present in the CT scan, the present work has the objective to perform the fissure segmentation using LBP texture measures and Neural Networks (NN). To implement the algorithm we used one MLP with 60 inputs, 120 hidden neurons and 2 output neurons. The input parameters for the network was the LBP histogram of the voxel being analyzed. For network training, it was necessary to create a system to label the features as fissures and non-fissures manually, where the user selects the fissure pixels class. To perform the validation of the algorithm was necessary to create a "gold standard" in which it was extracted a total of 100 images from 5 exams from the dataset LOLA11, where these images were the fissures were highlighted by two experts. From the gold standard, the proposed algorithm was processed and the results were obtained. For all tested images, the classifier obtained a better performance when the size of 15x15 pixels of the window was used to generate the histogram of the LBP. To get to this definition were tested sizes of 11x11, 15x15, 17x17 and 21x21 and the results were based on metrics ACC(%), TPR(%), SPC(%) distance mean and standard deviation of the distance. The first approach to analyze the results is through the voxels defined as fissure at the end of the proposed methodology. For the proposed methodology, using automatic detection and MLP LBP before thinning, the rates were obtained ACC = 96.7 %, TPR = 69.6 % and SPC = 96.8 % and ACC = 99 2 % TPR = 3 % and SPC = 99.81 % for the proposed method with the thinning in the end, considering the incidence of false positives and false negatives. Another approach used in the literature for evaluating methods of fissure segmentation is based on the average distance between the fissure delineated by the expert and the resulting fissure through the algorithm. Thus, the algorithm proposed in this paper was compared with the algorithm Lassen(2013) by the average distance between the manual segmented and the automatically segmented fissure. The proposed algorithm with the thinning in the end achieved a shorter distance average value and a lower standard deviation compared with the method of Lassen(2013). Finally, the results obtained for automatic segmentation of lung fissures are presented. The low incidence of false negative detections detection results, together with the significant reduction in false positive detections result in a high rate of settlement. We conclude that the segmentation technique for lung fissures is a useful target for pulmonary fissures on CT images and has potential to integrate systems that help medical diagnosis.
Full-text available
Thesis
Among all cancers, lung cancer (LC) is one of the most common tumors, an increase of 2% per year on its worldwide incidence. In Brazil, for the year of 2014, 27,330 new cases of LC are estimated, these being 16,400 in men and 10,930 in women. In this context, it is of fundamental importance for public health the identification on early stages of lung diseases. The diagnosis assistance shows to be important both from a clinical standpoint as in research. Among the factors contributing to this scene, one important is the increasing accuracy of diagnosis of a medical expert as you increase the number of information about the patient's condition. Thus, certain disorders might be detected early, including saving lives in some cases. The initial treatment for this disease consists of lobectomy. In this context, it is customary to perform the segmentation of lung lobes in CT images to extract data and assist in planning for lobectomy. The segmentation of the lobes from CT images is usually obtained by detection of pulmonary fissures. Thus, in order to obtain a more effective segmentation of pulmonary fissures, and perform a completely independent process from the other structures present in the CT scan, the present work has the objective to perform the fissure segmentation using LBP texture measures and Neural Networks (NN). To implement the algorithm we used one MLP with 60 inputs, 120 hidden neurons and 2 output neurons. The input parameters for the network was the LBP histogram of the voxel being analyzed. For network training, it was necessary to create a system to label the features as fissures and non-fissures manually, where the user selects the fissure pixels class. To perform the validation of the algorithm was necessary to create a "gold standard" in which it was extracted a total of 100 images from 5 exams from the dataset LOLA11, where these images were the fissures were highlighted by two experts. From the gold standard, the proposed algorithm was processed and the results were obtained. For all tested images, the classifier obtained a better performance when the size of 15x15 pixels of the window was used to generate the histogram of the LBP. To get to this definition were tested sizes of 11x11, 15x15, 17x17 and 21x21 and the results were based on metrics ACC(%), TPR(%), SPC(%) distance mean and standard deviation of the distance. The first approach to analyze the results is through the voxels defined as fissure at the end of the proposed methodology. For the proposed methodology, using automatic detection and MLP LBP before thinning, the rates were obtained ACC = 96.7 %, TPR = 69.6 % and SPC = 96.8 % and ACC = 99 2 % TPR = 3 % and SPC = 99.81 % for the proposed method with the thinning in the end, considering the incidence of false positives and false negatives. Another approach used in the literature for evaluating methods of fissure segmentation is based on the average distance between the fissure delineated by the expert and the resulting fissure through the algorithm. Thus, the algorithm proposed in this paper was compared with the algorithm Lassen(2013) by the average distance between the manual segmented and the automatically segmented fissure. The proposed algorithm with the thinning in the end achieved a shorter distance average value and a lower standard deviation compared with the method of Lassen(2013). Finally, the results obtained for automatic segmentation of lung fissures are presented. The low incidence of false negative detections detection results, together with the significant reduction in false positive detections result in a high rate of settlement. We conclude that the segmentation technique for lung fissures is a useful target for pulmonary fissures on CT images and has potential to integrate systems that help medical diagnosis.
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