Search results for: ad-hoc network routing protocol
4690 Impacts on Marine Ecosystems Using a Multilayer Network Approach
Authors: Nelson F. F. Ebecken, Gilberto C. Pereira, Lucio P. de Andrade
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Bays, estuaries and coastal ecosystems are some of the most used and threatened natural systems globally. Its deterioration is due to intense and increasing human activities. This paper aims to monitor the socio-ecological in Brazil, model and simulate it through a multilayer network representing a DPSIR structure (Drivers, Pressures, States-Impacts-Responses) considering the concept of Management based on Ecosystems to support decision-making under the National/State/Municipal Coastal Management policy. This approach considers several interferences and can represent a significant advance in several scientific aspects. The main objective of this paper is the coupling of three different types of complex networks, the first being an ecological network, the second a social network, and the third a network of economic activities, in order to model the marine ecosystem. Multilayer networks comprise two or more "layers", which may represent different types of interactions, different communities, different points in time, and so on. The dependency between layers results from processes that affect the various layers. For example, the dispersion of individuals between two patches affects the network structure of both samples. A multilayer network consists of (i) a set of physical nodes representing entities (e.g., species, people, companies); (ii) a set of layers, which may include multiple layering aspects (e.g., time dependency and multiple types of relationships); (iii) a set of state nodes, each of which corresponds to the manifestation of a given physical node in a layer-specific; and (iv) a set of edges (weighted or not) to connect the state nodes among themselves. The edge set includes the intralayer edges familiar and interlayer ones, which connect state nodes between layers. The applied methodology in an existent case uses the Flow cytometry process and the modeling of ecological relationships (trophic and non-trophic) following fuzzy theory concepts and graph visualization. The identification of subnetworks in the fuzzy graphs is carried out using a specific computational method. This methodology allows considering the influence of different factors and helps their contributions to the decision-making process.Keywords: marine ecosystems, complex systems, multilayer network, ecosystems management
Procedia PDF Downloads 1014689 Modeling Binomial Dependent Distribution of the Values: Synthesis Tables of Probabilities of Errors of the First and Second Kind of Biometrics-Neural Network Authentication System
Authors: B. S.Akhmetov, S. T. Akhmetova, D. N. Nadeyev, V. Yu. Yegorov, V. V. Smogoonov
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Estimated probabilities of errors of the first and second kind for nonideal biometrics-neural transducers 256 outputs, the construction of nomograms based error probability of 'own' and 'alien' from the mathematical expectation and standard deviation of the normalized measures Hamming.Keywords: modeling, errors, probability, biometrics, neural network, authentication
Procedia PDF Downloads 4754688 Social Interaction of Gifted Students in a Heterogeneous Educational Environment
Authors: Ekaterina Donii
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Understanding interpersonal competence, social interaction and peer relationships of gifted children is a concern for specialists in the field of gifted education. To gain more in-depth knowledge concerning the social functioning of gifted children among peers, we decided to study the social abilities of gifted children in a heterogeneous academic environment. Eight gifted children (5 of age 7, 1 of age 8.5, 1 of age 9.5 and 1 of age 10), their classmates (10 of age 7-8, 12 of age 8.5-9, 16 of age 9.5-10) and teachers participated in the study. The sociometric questionnaire analysis was based on the method of Rodríguez and Morera to check the social status of the gifted children among classmates. The Instrument Observational Protocol for Interactions within the Classroom (OPINTEC-v.5) was used to assess the social interactions between the gifted students, their classmates, and the teacher within the educational context. While doing a task together, the gifted children interacted more with popular and neither popular nor gifted classmates than with rejected classmates. While spending time together, the gifted children interacted more with neither popular nor rejected classmates than with popular or rejected classmates. All gifted children chose other gifted and non-gifted classmates for interaction, established close relations and demonstrated good social abilities interacting with their classmates. The aim of this study was to examine the social interactions, social status, and social network of the gifted students in a regular classroom. The majority of the gifted children were popular among their classmates and had good social skills. We should be alert, though, for those gifted children who do have social problems, in order to help them functioning in a regular classroom.Keywords: gifted, heterogeneous environment, sociometric status, social interactions
Procedia PDF Downloads 3394687 Quality of Service of Transportation Networks: A Hybrid Measurement of Travel Time and Reliability
Authors: Chin-Chia Jane
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In a transportation network, travel time refers to the transmission time from source node to destination node, whereas reliability refers to the probability of a successful connection from source node to destination node. With an increasing emphasis on quality of service (QoS), both performance indexes are significant in the design and analysis of transportation systems. In this work, we extend the well-known flow network model for transportation networks so that travel time and reliability are integrated into the QoS measurement simultaneously. In the extended model, in addition to the general arc capacities, each intermediate node has a time weight which is the travel time for per unit of commodity going through the node. Meanwhile, arcs and nodes are treated as binary random variables that switch between operation and failure with associated probabilities. For pre-specified travel time limitation and demand requirement, the QoS of a transportation network is the probability that source can successfully transport the demand requirement to destination while the total transmission time is under the travel time limitation. This work is pioneering, since existing literatures that evaluate travel time reliability via a single optimization path, the proposed QoS focuses the performance of the whole network system. To compute the QoS of transportation networks, we first transfer the extended network model into an equivalent min-cost max-flow network model. In the transferred network, each arc has a new travel time weight which takes value 0. Each intermediate node is replaced by two nodes u and v, and an arc directed from u to v. The newly generated nodes u and v are perfect nodes. The new direct arc has three weights: travel time, capacity, and operation probability. Then the universal set of state vectors is recursively decomposed into disjoint subsets of reliable, unreliable, and stochastic vectors until no stochastic vector is left. The decomposition is made possible by applying existing efficient min-cost max-flow algorithm. Because the reliable subsets are disjoint, QoS can be obtained directly by summing the probabilities of these reliable subsets. Computational experiments are conducted on a benchmark network which has 11 nodes and 21 arcs. Five travel time limitations and five demand requirements are set to compute the QoS value. To make a comparison, we test the exhaustive complete enumeration method. Computational results reveal the proposed algorithm is much more efficient than the complete enumeration method. In this work, a transportation network is analyzed by an extended flow network model where each arc has a fixed capacity, each intermediate node has a time weight, and both arcs and nodes are independent binary random variables. The quality of service of the transportation network is an integration of customer demands, travel time, and the probability of connection. We present a decomposition algorithm to compute the QoS efficiently. Computational experiments conducted on a prototype network show that the proposed algorithm is superior to existing complete enumeration methods.Keywords: quality of service, reliability, transportation network, travel time
Procedia PDF Downloads 2144686 Signal Restoration Using Neural Network Based Equalizer for Nonlinear channels
Authors: Z. Zerdoumi, D. Benatia, , D. Chicouche
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This paper investigates the application of artificial neural network to the problem of nonlinear channel equalization. The difficulties caused by channel distortions such as inter symbol interference (ISI) and nonlinearity can overcome by nonlinear equalizers employing neural networks. It has been shown that multilayer perceptron based equalizer outperform significantly linear equalizers. We present a multilayer perceptron based equalizer with decision feedback (MLP-DFE) trained with the back propagation algorithm. The capacity of the MLP-DFE to deal with nonlinear channels is evaluated. From simulation results it can be noted that the MLP based DFE improves significantly the restored signal quality, the steady state mean square error (MSE), and minimum Bit Error Rate (BER), when comparing with its conventional counterpart.Keywords: Artificial Neural Network, signal restoration, Nonlinear Channel equalization, equalization
Procedia PDF Downloads 4894685 Detecting Black Hole Attacks in Body Sensor Networks
Authors: Sara Alshehri, Bayan Alenzi, Atheer Alshehri, Samia Chelloug, Zainab Almry, Hussah Albugmai
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This paper concerns body area networks sensor that collect signals around a human body. The black hole attacks are the main security challenging problem because the data traffic can be dropped at any node. The focus of our proposed solution is to efficiently route data packets while detecting black hole nodes.Keywords: body sensor networks, security, black hole, routing, broadcasting, OMNeT++
Procedia PDF Downloads 6334684 Cells Detection and Recognition in Bone Marrow Examination with Deep Learning Method
Authors: Shiyin He, Zheng Huang
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In this paper, deep learning methods are applied in bio-medical field to detect and count different types of cells in an automatic way instead of manual work in medical practice, specifically in bone marrow examination. The process is mainly composed of two steps, detection and recognition. Mask-Region-Convolutional Neural Networks (Mask-RCNN) was used for detection and image segmentation to extract cells and then Convolutional Neural Networks (CNN), as well as Deep Residual Network (ResNet) was used to classify. Result of cell detection network shows high efficiency to meet application requirements. For the cell recognition network, two networks are compared and the final system is fully applicable.Keywords: cell detection, cell recognition, deep learning, Mask-RCNN, ResNet
Procedia PDF Downloads 1764683 A Constrained Neural Network Based Variable Neighborhood Search for the Multi-Objective Dynamic Flexible Job Shop Scheduling Problems
Authors: Aydin Teymourifar, Gurkan Ozturk, Ozan Bahadir
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In this paper, a new neural network based variable neighborhood search is proposed for the multi-objective dynamic, flexible job shop scheduling problems. The neural network controls the problems' constraints to prevent infeasible solutions, while the Variable Neighborhood Search (VNS) applies moves, based on the critical block concept to improve the solutions. Two approaches are used for managing the constraints, in the first approach, infeasible solutions are modified according to the constraints, after the moves application, while in the second one, infeasible moves are prevented. Several neighborhood structures from the literature with some modifications, also new structures are used in the VNS. The suggested neighborhoods are more systematically defined and easy to implement. Comparison is done based on a multi-objective flexible job shop scheduling problem that is dynamic because of the jobs different release time and machines breakdowns. The results show that the presented method has better performance than the compared VNSs selected from the literature.Keywords: constrained optimization, neural network, variable neighborhood search, flexible job shop scheduling, dynamic multi-objective optimization
Procedia PDF Downloads 3374682 Performance Estimation of Two Port Multiple-Input and Multiple-Output Antenna for Wireless Local Area Network Applications
Authors: Radha Tomar, Satish K. Jain, Manish Panchal, P. S. Rathore
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In the presented work, inset fed microstrip patch antenna (IFMPA) based two port MIMO Antenna system has been proposed, which is suitable for wireless local area network (WLAN) applications. IFMPA has been designed, optimized for 2.4 GHz and applied for MIMO formation. The optimized parameters of the proposed IFMPA have been used for fabrication of antenna and two port MIMO in a laboratory. Fabrication of the designed MIMO antenna has been done and tested experimentally for performance parameters like Envelope Correlation Coefficient (ECC), Mean Effective Gain (MEG), Directive Gain (DG), Channel Capacity Loss (CCL), Multiplexing Efficiency (ME) etc and results are compared with simulated parameters extracted with simulated S parameters to validate the results. The simulated and experimentally measured plots and numerical values of these MIMO performance parameters resembles very much with each other. This shows the success of MIMO antenna design methodology.Keywords: multiple-input and multiple-output, wireless local area network, vector network analyzer, envelope correlation coefficient
Procedia PDF Downloads 454681 An Application Framework for Integrating Wireless Sensor and Actuator Networks for Precision Farmingas Web of Things to Cloud Interface Using PaaS
Authors: Sumaya Ismail, Aijaz Ahmad Reshi
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The advances in sensor and embedded technologies have led to rapid developments in Wireless Sensor Networks (WSNs). Presently researchers focus on the integration of WSNs to the Internet for their pervasive availability to access these network resources as the interoperable subsystems. The recent computing technologies like cloud computing has made resource sharing as a converged infrastructure with required service interfaces for the shared resources over the Internet. This paper presents application architecture for wireless Sensor and Actuator Networks (WSANS) following web of things, which allows easy integration of each node to the Internet in order to provide them with web accessibility. The architecture enables the sensors and actuator nodes accessed and controlled using cloud interface on WWW. The application architecture was implemented using existing web and its emerging technologies. In particular, the Representational State Transfer protocol (REST) was extended for the specific requirements of the application. The Cloud computing environment has been used as a development platform for the application to assess the possibility of integrating the WSAN nodes to Cloud services. The mushroom farm environment monitoring and control using WSANs has been taken as a research use case.Keywords: WSAN, REST, web of things, ZigBee, cloud interface, PaaS, sensor gateway
Procedia PDF Downloads 934680 Explainable Graph Attention Networks
Authors: David Pham, Yongfeng Zhang
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Graphs are an important structure for data storage and computation. Recent years have seen the success of deep learning on graphs such as Graph Neural Networks (GNN) on various data mining and machine learning tasks. However, most of the deep learning models on graphs cannot easily explain their predictions and are thus often labelled as “black boxes.” For example, Graph Attention Network (GAT) is a frequently used GNN architecture, which adopts an attention mechanism to carefully select the neighborhood nodes for message passing and aggregation. However, it is difficult to explain why certain neighbors are selected while others are not and how the selected neighbors contribute to the final classification result. In this paper, we present a graph learning model called Explainable Graph Attention Network (XGAT), which integrates graph attention modeling and explainability. We use a single model to target both the accuracy and explainability of problem spaces and show that in the context of graph attention modeling, we can design a unified neighborhood selection strategy that selects appropriate neighbor nodes for both better accuracy and enhanced explainability. To justify this, we conduct extensive experiments to better understand the behavior of our model under different conditions and show an increase in both accuracy and explainability.Keywords: explainable AI, graph attention network, graph neural network, node classification
Procedia PDF Downloads 1764679 Plate-Laminated Slotted-Waveguide Fed 2×3 Planar Inverted F Antenna Array
Authors: Badar Muneer, Waseem Shabir, Faisal Karim Shaikh
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Substrate Integrated waveguide based 6-element array of Planar Inverted F antenna (PIFA) has been presented and analyzed parametrically in this paper. The antenna is fed with coupled transverse slots on a plate laminated waveguide cavity to ensure wide bandwidth and simplicity of feeding network. The two-layer structure has one layer dedicated for feeding network and the top layer dedicated for radiating elements. It has been demonstrated that the presented feeding technique for feeding such class of array antennas can be far simple in structure and miniaturized in size when it comes to designing large phased array antenna systems. A good return loss and standing wave ratio of 2:1 has been achieved while maintaining properties of typical PIFA.Keywords: feeding network, laminated waveguide, PIFA, transverse slots
Procedia PDF Downloads 3024678 Advanced Concrete Crack Detection Using Light-Weight MobileNetV2 Neural Network
Authors: Li Hui, Riyadh Hindi
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Concrete structures frequently suffer from crack formation, a critical issue that can significantly reduce their lifespan by allowing damaging agents to enter. Traditional methods of crack detection depend on manual visual inspections, which heavily relies on the experience and expertise of inspectors using tools. In this study, a more efficient, computer vision-based approach is introduced by using the lightweight MobileNetV2 neural network. A dataset of 40,000 images was used to develop a specialized crack evaluation algorithm. The analysis indicates that MobileNetV2 matches the accuracy of traditional CNN methods but is more efficient due to its smaller size, making it well-suited for mobile device applications. The effectiveness and reliability of this new method were validated through experimental testing, highlighting its potential as an automated solution for crack detection in concrete structures.Keywords: Concrete crack, computer vision, deep learning, MobileNetV2 neural network
Procedia PDF Downloads 634677 Dry Relaxation Shrinkage Prediction of Bordeaux Fiber Using a Feed Forward Neural
Authors: Baeza S. Roberto
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The knitted fabric suffers a deformation in its dimensions due to stretching and tension factors, transverse and longitudinal respectively, during the process in rectilinear knitting machines so it performs a dry relaxation shrinkage procedure and thermal action of prefixed to obtain stable conditions in the knitting. This paper presents a dry relaxation shrinkage prediction of Bordeaux fiber using a feed forward neural network and linear regression models. Six operational alternatives of shrinkage were predicted. A comparison of the results was performed finding neural network models with higher levels of explanation of the variability and prediction. The presence of different reposes are included. The models were obtained through a neural toolbox of Matlab and Minitab software with real data in a knitting company of Southern Guanajuato. The results allow predicting dry relaxation shrinkage of each alternative operation.Keywords: neural network, dry relaxation, knitting, linear regression
Procedia PDF Downloads 5714676 IoT: State-of-the-Art and Future Directions
Authors: Bashir Abdu Muzakkari, Aisha Umar Sulaiman, Mohamed Afendee Muhamad, Sanah Abdullahi Muaz
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The field of the Internet of Things (IoT) is rapidly expanding and has the potential to completely change how we work, live, and interact with the world. The Internet of Things (IoT) is the term used to describe a network of networked physical objects, including machinery, vehicles, and buildings, which are equipped with electronics, software, sensors, and network connectivity. This review paper aims to provide a comprehensive overview of the current state of IoT, including its definition, key components, development history, and current applications. The paper will also discuss the challenges and opportunities presented by IoT, as well as its potential impact on various industries, such as healthcare, agriculture, and transportation. In addition, this paper will highlight the ethical and security concerns associated with IoT and the need for effective solutions to address these challenges. The paper concludes by highlighting the prospects of IoT and the directions for future research in this field.Keywords: internet of things, IoT, sensors, network
Procedia PDF Downloads 1634675 Association of Post-Traumatic Stress Disorder with Work Performance amongst Emergency Medical Service Personnel, Karachi, Pakistan
Authors: Salima Kerai, Muhammad Islam, Uzma Khan, Nargis Asad, Junaid Razzak, Omrana Pasha
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Background: Pre-hospital care providers are exposed to various kinds of stressors. Their daily exposure to diverse critical and traumatic incidents can lead to stress reactions like Post-Traumatic Stress Disorder (PTSD). Consequences of PTSD in terms of work loss can be catastrophic because of its compound effect on families, which affect them economically, socially and emotionally. Therefore, it is critical to assess the association between PTSD and Work performance in Emergency Medical Service (EMS) if exist any. Methods: This prospective observational study was carried out at AMAN EMS in Karachi, Pakistan. EMS personnel were screened for potential PTSD using impact of event scale-revised (IES-R). Work performance was assessed on basis of five variables; number of late arrivals to work, number of days absent, number of days sick, adherence to protocol and patient satisfaction survey over the period of 3 months. In order to model outcomes like number of late arrivals to work, days absent and days late; negative binomial regression was used whereas logistic regression was applied for adherence to protocol and linear for patient satisfaction scores. Results: Out of 536 EMS personnel, 525 were found to be eligible, of them 518 consented. However data on 507 were included because 7 left the job during study period. The mean score of PTSD was found to be 24.0 ± 12.2. However, weak and insignificant association was found between PTSD and work performance measures: number of late arrivals (RRadj 0.99; 95% CI 0.98-1.00), days absent (RRadj 0.98; 95% CI 0.96-0.99), days sick (Rradj 0.99; 95% CI 0.98 to 1.00), adherence to protocol (ORadj 1.01: 95% CI 0.99 to 1.04) and patient satisfaction (0.001% score; 95% CI -0.03% to 0.03%). Conclusion: No association was found between PTSD and Work performance in the selected EMS population in Karachi Pakistan. Further studies are needed to explore the phenomenon of resiliency in these populations. Moreover, qualitative work is required to explore perceptions and feelings like willingness to go to work, readiness to carry out job responsibilities.Keywords: trauma, emergency medical service, stress, pakistan
Procedia PDF Downloads 3274674 Toward an Understanding of the Neurofunctional Dissociation between Animal and Tool Concepts: A Graph Theoretical Analysis
Authors: Skiker Kaoutar, Mounir Maouene
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Neuroimaging studies have shown that animal and tool concepts rely on distinct networks of brain areas. Animal concepts depend predominantly on temporal areas while tool concepts rely on fronto-temporo-parietal areas. However, the origin of this neurofunctional distinction for processing animal and tool concepts remains still unclear. Here, we address this question from a network perspective suggesting that the neural distinction between animals and tools might reflect the differences in their structural semantic networks. We build semantic networks for animal and tool concepts derived from Mc Rae and colleagues’s behavioral study conducted on a large number of participants. These two networks are thus analyzed through a large number of graph theoretical measures for small-worldness: centrality, clustering coefficient, average shortest path length, as well as resistance to random and targeted attacks. The results indicate that both animal and tool networks have small-world properties. More importantly, the animal network is more vulnerable to targeted attacks compared to the tool network a result that correlates with brain lesions studies.Keywords: animals, tools, network, semantics, small-world, resilience to damage
Procedia PDF Downloads 5354673 Nonlinear Modeling of the PEMFC Based on NNARX Approach
Authors: Shan-Jen Cheng, Te-Jen Chang, Kuang-Hsiung Tan, Shou-Ling Kuo
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Polymer Electrolyte Membrane Fuel Cell (PEMFC) is such a time-vary nonlinear dynamic system. The traditional linear modeling approach is hard to estimate structure correctly of PEMFC system. From this reason, this paper presents a nonlinear modeling of the PEMFC using Neural Network Auto-regressive model with eXogenous inputs (NNARX) approach. The multilayer perception (MLP) network is applied to evaluate the structure of the NNARX model of PEMFC. The validity and accuracy of NNARX model are tested by one step ahead relating output voltage to input current from measured experimental of PEMFC. The results show that the obtained nonlinear NNARX model can efficiently approximate the dynamic mode of the PEMFC and model output and system measured output consistently.Keywords: PEMFC, neural network, nonlinear modeling, NNARX
Procedia PDF Downloads 3704672 Software-Defined Networking: A New Approach to Fifth Generation Networks: Security Issues and Challenges Ahead
Authors: Behrooz Daneshmand
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Software Defined Networking (SDN) is designed to meet the future needs of 5G mobile networks. The SDN architecture offers a new solution that involves separating the control plane from the data plane, which is usually paired together. Network functions traditionally performed on specific hardware can now be abstracted and virtualized on any device, and a centralized software-based administration approach is based on a central controller, facilitating the development of modern applications and services. These plan standards clear the way for a more adaptable, speedier, and more energetic network beneath computer program control compared with a conventional network. We accept SDN gives modern inquire about openings to security, and it can significantly affect network security research in numerous diverse ways. Subsequently, the SDN architecture engages systems to effectively screen activity and analyze threats to facilitate security approach modification and security benefit insertion. The segregation of the data planes and control and, be that as it may, opens security challenges, such as man-in-the-middle attacks (MIMA), denial of service (DoS) attacks, and immersion attacks. In this paper, we analyze security threats to each layer of SDN - application layer - southbound interfaces/northbound interfaces - controller layer and data layer. From a security point of see, the components that make up the SDN architecture have a few vulnerabilities, which may be abused by aggressors to perform noxious activities and hence influence the network and its administrations. Software-defined network assaults are shockingly a reality these days. In a nutshell, this paper highlights architectural weaknesses and develops attack vectors at each layer, which leads to conclusions about further progress in identifying the consequences of attacks and proposing mitigation strategies.Keywords: software-defined networking, security, SDN, 5G/IMT-2020
Procedia PDF Downloads 894671 BlueVision: A Visual Tool for Exploring a Blockchain Network
Authors: Jett Black, Jordyn Godsey, Gaby G. Dagher, Steve Cutchin
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Despite the growing interest in distributed ledger technology, many data visualizations of blockchain are limited to monotonous tabular displays or overly abstract graphical representations that fail to adequately educate individuals on blockchain components and their functionalities. To address these limitations, it is imperative to develop data visualizations that offer not only comprehensive insights into these domains but education as well. This research focuses on providing a conceptual understanding of the consensus process that underlies blockchain technology. This is accomplished through the implementation of a dynamic network visualization and an interactive educational tool called BlueVision. Further, a controlled user study is conducted to measure the effectiveness and usability of BlueVision. The findings demonstrate that the tool represents significant advancements in the field of blockchain visualization, effectively catering to the educational needs of both novice and proficient users.Keywords: blockchain, visualization, consensus, distributed network
Procedia PDF Downloads 554670 Understanding and Improving Neural Network Weight Initialization
Authors: Diego Aguirre, Olac Fuentes
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In this paper, we present a taxonomy of weight initialization schemes used in deep learning. We survey the most representative techniques in each class and compare them in terms of overhead cost, convergence rate, and applicability. We also introduce a new weight initialization scheme. In this technique, we perform an initial feedforward pass through the network using an initialization mini-batch. Using statistics obtained from this pass, we initialize the weights of the network, so the following properties are met: 1) weight matrices are orthogonal; 2) ReLU layers produce a predetermined number of non-zero activations; 3) the output produced by each internal layer has a unit variance; 4) weights in the last layer are chosen to minimize the error in the initial mini-batch. We evaluate our method on three popular architectures, and a faster converge rates are achieved on the MNIST, CIFAR-10/100, and ImageNet datasets when compared to state-of-the-art initialization techniques.Keywords: deep learning, image classification, supervised learning, weight initialization
Procedia PDF Downloads 1244669 Proposal of Optimality Evaluation for Quantum Secure Communication Protocols by Taking the Average of the Main Protocol Parameters: Efficiency, Security and Practicality
Authors: Georgi Bebrov, Rozalina Dimova
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In the field of quantum secure communication, there is no evaluation that characterizes quantum secure communication (QSC) protocols in a complete, general manner. The current paper addresses the problem concerning the lack of such an evaluation for QSC protocols by introducing an optimality evaluation, which is expressed as the average over the three main parameters of QSC protocols: efficiency, security, and practicality. For the efficiency evaluation, the common expression of this parameter is used, which incorporates all the classical and quantum resources (bits and qubits) utilized for transferring a certain amount of information (bits) in a secure manner. By using criteria approach whether or not certain criteria are met, an expression for the practicality evaluation is presented, which accounts for the complexity of the QSC practical realization. Based on the error rates that the common quantum attacks (Measurement and resend, Intercept and resend, probe attack, and entanglement swapping attack) induce, the security evaluation for a QSC protocol is proposed as the minimum function taken over the error rates of the mentioned quantum attacks. For the sake of clarity, an example is presented in order to show how the optimality is calculated.Keywords: quantum cryptography, quantum secure communcation, quantum secure direct communcation security, quantum secure direct communcation efficiency, quantum secure direct communcation practicality
Procedia PDF Downloads 1764668 Critical Evaluation and Analysis of Effects of Different Queuing Disciplines on Packets Delivery and Delay for Different Applications
Authors: Omojokun Gabriel Aju
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Communication network is a process of exchanging data between two or more devices via some forms of transmission medium using communication protocols. The data could be in form of text, images, audio, video or numbers which can be grouped into FTP, Email, HTTP, VOIP or Video applications. The effectiveness of such data exchange will be proved if they are accurately delivered within specified time. While some senders will not really mind when the data is actually received by the receiving device, inasmuch as it is acknowledged to have been received by the receiver. The time a data takes to get to a receiver could be very important to another sender, as any delay could cause serious problem or even in some cases rendered the data useless. The validity or invalidity of a data after delay will therefore definitely depend on the type of data (information). It is therefore imperative for the network device (such as router) to be able to differentiate among the packets which are time sensitive and those that are not, when they are passing through the same network. So, here is where the queuing disciplines comes to play, to handle network resources when such network is designed to service widely varying types of traffics and manage the available resources according to the configured policies. Therefore, as part of the resources allocation mechanisms, a router within the network must implement some queuing discipline that governs how packets (data) are buffered while waiting to be transmitted. The implementation of the queuing discipline will regulate how the packets are buffered while waiting to be transmitted. In achieving this, various queuing disciplines are being used to control the transmission of these packets, by determining which of the packets get the highest priority, less priority and which packets are dropped. The queuing discipline will therefore control the packets latency by determining how long a packet can wait to be transmitted or dropped. The common queuing disciplines are first-in-first-out queuing, Priority queuing and Weighted-fair queuing (FIFO, PQ and WFQ). This paper critically evaluates and analyse through the use of Optimized Network Evaluation Tool (OPNET) Modeller, Version 14.5 the effects of three queuing disciplines (FIFO, PQ and WFQ) on the performance of 5 different applications (FTP, HTTP, E-Mail, Voice and Video) within specified parameters using packets sent, packets received and transmission delay as performance metrics. The paper finally suggests some ways in which networks can be designed to provide better transmission performance while using these queuing disciplines.Keywords: applications, first-in-first-out queuing (FIFO), optimised network evaluation tool (OPNET), packets, priority queuing (PQ), queuing discipline, weighted-fair queuing (WFQ)
Procedia PDF Downloads 3494667 Artificial Neural Network in Ultra-High Precision Grinding of Borosilicate-Crown Glass
Authors: Goodness Onwuka, Khaled Abou-El-Hossein
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Borosilicate-crown (BK7) glass has found broad application in the optic and automotive industries and the growing demands for nanometric surface finishes is becoming a necessity in such applications. Thus, it has become paramount to optimize the parameters influencing the surface roughness of this precision lens. The research was carried out on a 4-axes Nanoform 250 precision lathe machine with an ultra-high precision grinding spindle. The experiment varied the machining parameters of feed rate, wheel speed and depth of cut at three levels for different combinations using Box Behnken design of experiment and the resulting surface roughness values were measured using a Taylor Hobson Dimension XL optical profiler. Acoustic emission monitoring technique was applied at a high sampling rate to monitor the machining process while further signal processing and feature extraction methods were implemented to generate the input to a neural network algorithm. This paper highlights the training and development of a back propagation neural network prediction algorithm through careful selection of parameters and the result show a better classification accuracy when compared to a previously developed response surface model with very similar machining parameters. Hence artificial neural network algorithms provide better surface roughness prediction accuracy in the ultra-high precision grinding of BK7 glass.Keywords: acoustic emission technique, artificial neural network, surface roughness, ultra-high precision grinding
Procedia PDF Downloads 3004666 A New Graph Theoretic Problem with Ample Practical Applications
Authors: Mehmet Hakan Karaata
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In this paper, we first coin a new graph theocratic problem with numerous applications. Second, we provide two algorithms for the problem. The first solution is using a brute-force techniques, whereas the second solution is based on an initial identification of the cycles in the given graph. We then provide a correctness proof of the algorithm. The applications of the problem include graph analysis, graph drawing and network structuring.Keywords: algorithm, cycle, graph algorithm, graph theory, network structuring
Procedia PDF Downloads 3764665 Proposed Fault Detection Scheme on Low Voltage Distribution Feeders
Authors: Adewusi Adeoluwawale, Oronti Iyabosola Busola, Akinola Iretiayo, Komolafe Olusola Aderibigbe
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The complex and radial structure of the low voltage distribution network (415V) makes it vulnerable to faults which are due to system and the environmental related factors. Besides these, the protective scheme employed on the low voltage network which is the fuse cannot be monitored remotely such that in the event of sustained fault, the utility will have to rely solely on the complaint brought by customers for loss of supply and this tends to increase the length of outages. A microcontroller based fault detection scheme is hereby developed to detect low voltage and high voltage fault conditions which are common faults on this network. Voltages below 198V and above 242V on the distribution feeders are classified and detected as low voltage and high voltages respectively. Results shows that the developed scheme produced a good response time in the detection of these faults.Keywords: fault detection, low voltage distribution feeders, outage times, sustained faults
Procedia PDF Downloads 5324664 Hybrid Hierarchical Clustering Approach for Community Detection in Social Network
Authors: Radhia Toujani, Jalel Akaichi
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Social Networks generally present a hierarchy of communities. To determine these communities and the relationship between them, detection algorithms should be applied. Most of the existing algorithms, proposed for hierarchical communities identification, are based on either agglomerative clustering or divisive clustering. In this paper, we present a hybrid hierarchical clustering approach for community detection based on both bottom-up and bottom-down clustering. Obviously, our approach provides more relevant community structure than hierarchical method which considers only divisive or agglomerative clustering to identify communities. Moreover, we performed some comparative experiments to enhance the quality of the clustering results and to show the effectiveness of our algorithm.Keywords: agglomerative hierarchical clustering, community structure, divisive hierarchical clustering, hybrid hierarchical clustering, opinion mining, social network, social network analysis
Procedia PDF Downloads 3534663 GA3C for Anomalous Radiation Source Detection
Authors: Chia-Yi Liu, Bo-Bin Xiao, Wen-Bin Lin, Hsiang-Ning Wu, Liang-Hsun Huang
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In order to reduce the risk of radiation damage that personnel may suffer during operations in the radiation environment, the use of automated guided vehicles to assist or replace on-site personnel in the radiation environment has become a key technology and has become an important trend. In this paper, we demonstrate our proof of concept for autonomous self-learning radiation source searcher in an unknown environment without a map. The research uses GPU version of Asynchronous Advantage Actor-Critic network (GA3C) of deep reinforcement learning to search for radiation sources. The searcher network, based on GA3C architecture, has self-directed learned and improved how search the anomalous radiation source by training 1 million episodes under three simulation environments. In each episode of training, the radiation source position, the radiation source intensity, starting position, are all set randomly in one simulation environment. The input for searcher network is the fused data from a 2D laser scanner and a RGB-D camera as well as the value of the radiation detector. The output actions are the linear and angular velocities. The searcher network is trained in a simulation environment to accelerate the learning process. The well-performance searcher network is deployed to the real unmanned vehicle, Dashgo E2, which mounts LIDAR of YDLIDAR G4, RGB-D camera of Intel D455, and radiation detector made by Institute of Nuclear Energy Research. In the field experiment, the unmanned vehicle is enable to search out the radiation source of the 18.5MBq Na-22 by itself and avoid obstacles simultaneously without human interference.Keywords: deep reinforcement learning, GA3C, source searching, source detection
Procedia PDF Downloads 1034662 An Application Framework for Integrating Wireless Sensor and Actuator Networks for Precision Farming as Web of Things to Cloud Interface Using Platform as a Service
Authors: Sumaya Iqbal, Aijaz Ahmad Reshi
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The advances in sensor and embedded technologies have led to rapid developments in Wireless Sensor Networks (WSNs). Presently researchers focus on the integration of WSNs to Internet for their pervasive availability to access these network resources as the interoperable subsystems. The recent computing technologies like cloud computing has made the resource sharing as a converged infrastructure with required service interfaces for the shared resources over the Internet. This paper presents application architecture for wireless Sensor and Actuator Networks (WSANS) following web of things, which allows easy integration of each node to the Internet in order to provide them web accessibility. The architecture enables the sensors and actuator nodes accessed and controlled using cloud interface on WWW. The application architecture was implemented using existing web and its emerging technologies. In particular Representational State Transfer protocol (REST) was extended for the specific requirements of the application. Cloud computing environment has been used as a development platform for the application to assess the possibility of integrating the WSAN nodes to Cloud services. The mushroom farm environment monitoring and control using WSANs has been taken as a research use case.Keywords: WSAN, REST, web of things, ZigBee, cloud interface, PaaS, sensor gateway
Procedia PDF Downloads 1104661 Artificial Neural Network Reconstruction of Proton Exchange Membrane Fuel Cell Output Profile under Transient Operation
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Unbalanced power output from individual cells of Proton Exchange Membrane Fuel Cell (PEMFC) has direct effects on PEMFC stack performance, in particular under transient operation. In the paper, a multi-layer ANN (Artificial Neural Network) model Radial Basis Functions (RBF) has been developed for predicting cells' output profiles by applying gas supply parameters, cooling conditions, temperature measurement of individual cells, etc. The feed-forward ANN model was validated with experimental data. Influence of relevant parameters of RBF on the network accuracy was investigated. After adequate model training, the modelling results show good correspondence between actual measurements and reconstructed output profiles. Finally, after the model was used to optimize the stack output performance under steady-state and transient operating conditions, it suggested that the developed ANN control model can help PEMFC stack to have obvious improvement on power output under fast acceleration process.Keywords: proton exchange membrane fuel cell, PEMFC, artificial neural network, ANN, cell output profile, transient
Procedia PDF Downloads 157