Search results for: Siamese networks
1246 Factorial Design Analysis for Quality of Video on MANET
Authors: Hyoup-Sang Yoon
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The quality of video transmitted by mobile ad hoc networks (MANETs) can be influenced by several factors, including protocol layers; parameter settings of each protocol. In this paper, we are concerned with understanding the functional relationship between these influential factors and objective video quality in MANETs. We illustrate a systematic statistical design of experiments (DOE) strategy can be used to analyze MANET parameters and performance. Using a 2k factorial design, we quantify the main and interactive effects of 7 factors on a response metric (i.e., mean opinion score (MOS) calculated by PSNR with Evalvid package) we then develop a first-order linear regression model between the influential factors and the performance metric.
Keywords: Evalvid, full factorial design, mobile ad hoc networks, ns-2.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20781245 On the Analysis of Localization Accuracy of Wireless Indoor Positioning Systems using Cramer's Rule
Authors: Kriangkrai Maneerat, Chutima Prommak
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This paper presents an analysis of the localization accuracy of indoor positioning systems using Cramer-s rule via IEEE 802.15.4 wireless sensor networks. The objective is to study the impact of the methods used to convert the received signal strength into the distance that is used to compute the object location in the wireless indoor positioning system. Various methods were tested and the localization accuracy was analyzed. The experimental results show that the method based on the empirical data measured in the non line-of-sight (NLOS) environment yield the highest localization accuracy; with the minimum error distance less than 3 m.
Keywords: Indoor positioning systems, localization accuracy, wireless networks, Cramer's rule.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19561244 Software Maintenance Severity Prediction for Object Oriented Systems
Authors: Parvinder S. Sandhu, Roma Jaswal, Sandeep Khimta, Shailendra Singh
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As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done in time especially for the critical applications. As, Neural networks, which have been already applied in software engineering applications to build reliability growth models predict the gross change or reusability metrics. Neural networks are non-linear sophisticated modeling techniques that are able to model complex functions. Neural network techniques are used when exact nature of input and outputs is not known. A key feature is that they learn the relationship between input and output through training. In this present work, various Neural Network Based techniques are explored and comparative analysis is performed for the prediction of level of need of maintenance by predicting level severity of faults present in NASA-s public domain defect dataset. The comparison of different algorithms is made on the basis of Mean Absolute Error, Root Mean Square Error and Accuracy Values. It is concluded that Generalized Regression Networks is the best algorithm for classification of the software components into different level of severity of impact of the faults. The algorithm can be used to develop model that can be used for identifying modules that are heavily affected by the faults.Keywords: Neural Network, Software faults, Software Metric.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15651243 Oscillation Effect of the Multi-stage Learning for the Layered Neural Networks and Its Analysis
Authors: Isao Taguchi, Yasuo Sugai
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This paper proposes an efficient learning method for the layered neural networks based on the selection of training data and input characteristics of an output layer unit. Comparing to recent neural networks; pulse neural networks, quantum neuro computation, etc, the multilayer network is widely used due to its simple structure. When learning objects are complicated, the problems, such as unsuccessful learning or a significant time required in learning, remain unsolved. Focusing on the input data during the learning stage, we undertook an experiment to identify the data that makes large errors and interferes with the learning process. Our method devides the learning process into several stages. In general, input characteristics to an output layer unit show oscillation during learning process for complicated problems. The multi-stage learning method proposes by the authors for the function approximation problems of classifying learning data in a phased manner, focusing on their learnabilities prior to learning in the multi layered neural network, and demonstrates validity of the multi-stage learning method. Specifically, this paper verifies by computer experiments that both of learning accuracy and learning time are improved of the BP method as a learning rule of the multi-stage learning method. In learning, oscillatory phenomena of a learning curve serve an important role in learning performance. The authors also discuss the occurrence mechanisms of oscillatory phenomena in learning. Furthermore, the authors discuss the reasons that errors of some data remain large value even after learning, observing behaviors during learning.
Keywords: data selection, function approximation problem, multistage leaning, neural network, voluntary oscillation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14221242 Emotion Detection in Twitter Messages Using Combination of Long Short-Term Memory and Convolutional Deep Neural Networks
Authors: B. Golchin, N. Riahi
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One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.
Keywords: emotion classification, sentiment analysis, social networks, deep neural networks
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6471241 System Identification with General Dynamic Neural Networks and Network Pruning
Authors: Christian Endisch, Christoph Hackl, Dierk Schröder
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This paper presents an exact pruning algorithm with adaptive pruning interval for general dynamic neural networks (GDNN). GDNNs are artificial neural networks with internal dynamics. All layers have feedback connections with time delays to the same and to all other layers. The structure of the plant is unknown, so the identification process is started with a larger network architecture than necessary. During parameter optimization with the Levenberg- Marquardt (LM) algorithm irrelevant weights of the dynamic neural network are deleted in order to find a model for the plant as simple as possible. The weights to be pruned are found by direct evaluation of the training data within a sliding time window. The influence of pruning on the identification system depends on the network architecture at pruning time and the selected weight to be deleted. As the architecture of the model is changed drastically during the identification and pruning process, it is suggested to adapt the pruning interval online. Two system identification examples show the architecture selection ability of the proposed pruning approach.Keywords: System identification, dynamic neural network, recurrentneural network, GDNN, optimization, Levenberg Marquardt, realtime recurrent learning, network pruning, quasi-online learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19251240 A Novel Approach to Fault Classification and Fault Location for Medium Voltage Cables Based on Artificial Neural Network
Authors: H. Khorashadi-Zadeh, M. R. Aghaebrahimi
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A novel application of neural network approach to fault classification and fault location of Medium voltage cables is demonstrated in this paper. Different faults on a protected cable should be classified and located correctly. This paper presents the use of neural networks as a pattern classifier algorithm to perform these tasks. The proposed scheme is insensitive to variation of different parameters such as fault type, fault resistance, and fault inception angle. Studies show that the proposed technique is able to offer high accuracy in both of the fault classification and fault location tasks.Keywords: Artificial neural networks, cable, fault location andfault classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18351239 Health Risk Assessment in Lead Battery Smelter Factory: A Bayesian Belief Network Method
Authors: Kevin Fong-Rey Liu, Ken Yeh, Cheng-Wu Chen, Han-Hsi Liang
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This paper proposes the use of Bayesian belief networks (BBN) as a higher level of health risk assessment for a dumping site of lead battery smelter factory. On the basis of the epidemiological studies, the actual hospital attendance records and expert experiences, the BBN is capable of capturing the probabilistic relationships between the hazardous substances and their adverse health effects, and accordingly inferring the morbidity of the adverse health effects. The provision of the morbidity rates of the related diseases is more informative and can alleviate the drawbacks of conventional methods.Keywords: Bayesian belief networks, lead battery smelter factory, health risk assessment.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17121238 A Systems Approach to Gene Ranking from DNA Microarray Data of Cervical Cancer
Authors: Frank Emmert Streib, Matthias Dehmer, Jing Liu, Max Mühlhauser
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In this paper we present a method for gene ranking from DNA microarray data. More precisely, we calculate the correlation networks, which are unweighted and undirected graphs, from microarray data of cervical cancer whereas each network represents a tissue of a certain tumor stage and each node in the network represents a gene. From these networks we extract one tree for each gene by a local decomposition of the correlation network. The interpretation of a tree is that it represents the n-nearest neighbor genes on the n-th level of a tree, measured by the Dijkstra distance, and, hence, gives the local embedding of a gene within the correlation network. For the obtained trees we measure the pairwise similarity between trees rooted by the same gene from normal to cancerous tissues. This evaluates the modification of the tree topology due to progression of the tumor. Finally, we rank the obtained similarity values from all tissue comparisons and select the top ranked genes. For these genes the local neighborhood in the correlation networks changes most between normal and cancerous tissues. As a result we find that the top ranked genes are candidates suspected to be involved in tumor growth and, hence, indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer.Keywords: Graph similarity, DNA microarray data, cancer.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17451237 Power Quality Evaluation of Electrical Distribution Networks
Authors: Mohamed Idris S. Abozaed, Suliman Mohamed Elrajoubi
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Researches and concerns in power quality gained significant momentum in the field of power electronics systems over the last two decades globally. This sudden increase in the number of concerns over power quality problems is a result of the huge increase in the use of non-linear loads. In this paper, power quality evaluation of some distribution networks at Misurata - Libya has been done using a power quality and energy analyzer (Fluke 437 Series II). The results of this evaluation are used to minimize the problems of power quality. The analysis shows the main power quality problems that exist and the level of awareness of power quality issues with the aim of generating a start point which can be used as guidelines for researchers and end users in the field of power systems.
Keywords: Power Quality Disturbances, Power Quality Evaluation, Statistical Analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 31851236 Big Data Strategy for Telco: Network Transformation
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Big data has the potential to improve the quality of services; enable infrastructure that businesses depend on to adapt continually and efficiently; improve the performance of employees; help organizations better understand customers; and reduce liability risks. Analytics and marketing models of fixed and mobile operators are falling short in combating churn and declining revenue per user. Big Data presents new method to reverse the way and improve profitability. The benefits of Big Data and next-generation network, however, are more exorbitant than improved customer relationship management. Next generation of networks are in a prime position to monetize rich supplies of customer information—while being mindful of legal and privacy issues. As data assets are transformed into new revenue streams will become integral to high performance.
Keywords: Big Data, Next Generation Networks, Network Transformation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25031235 Performance Evaluation of TCP Vegas versus Different TCP Variants in Homogeneous and Heterogeneous Wired Networks
Authors: B. S. Yew, B. L. Ong, R. B. Ahmad
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A study on the performance of TCP Vegas versus different TCP variants in homogeneous and heterogeneous wired networks are performed via simulation experiment using network simulator (ns-2). This performance evaluation prepared a comparison medium for the performance evaluation of enhanced-TCP Vegas in wired network and for wireless network. In homogeneous network, the performance of TCP Tahoe, TCP Reno, TCP NewReno, TCP Vegas and TCP SACK are analyzed. In heterogeneous network, the performances of TCP Vegas against TCP variants are analyzed. TCP Vegas outperforms other TCP variants in homogeneous wired network. However, TCP Vegas achieves unfair throughput in heterogeneous wired network.Keywords: TCP Vegas, Homogeneous, Heterogeneous, WiredNetwork.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17021234 Information Fusion as a Means of Forecasting Expenditures for Regenerating Complex Investment Goods
Authors: Steffen C. Eickemeyer, Tim Borcherding, Peter Nyhuis, Hannover
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Planning capacities when regenerating complex investment goods involves particular challenges in that the planning is subject to a large degree of uncertainty regarding load information. Using information fusion – by applying Bayesian Networks – a method is being developed for forecasting the anticipated expenditures (human labor, tool and machinery utilization, time etc.) for regenerating a good. The generated forecasts then later serve as a tool for planning capacities and ensure a greater stability in the planning processes.
Keywords: Bayesian networks, capacity planning, complex investment goods, damages library, forecasting, information fusion, regeneration.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16211233 Routing Load Analysis over 802.11 DCF of Reactive Routing Protocols DSR and DYMO
Authors: Parma Nand, S.C. Sharma
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The Mobile Ad-hoc Network (MANET) is a collection of self-configuring and rapidly deployed mobile nodes (routers) without any central infrastructure. Routing is one of the potential issues. Many routing protocols are reported but it is difficult to decide which one is best in all scenarios. In this paper on demand routing protocols DSR and DYMO based on IEEE 802.11 DCF MAC protocol are examined and characteristic summary of these routing protocols is presented. Their performance is analyzed and compared on performance measuring metrics throughput, dropped packets due to non availability of routes, duplicate RREQ generated for route discovery and normalized routing load by varying CBR data traffic load using QualNet 5.0.2 network simulator.
Keywords: Adhoc networks, wireless networks, CBR, routingprotocols, route discovery, simulation, performance evaluation, MAC, IEEE 802.11.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17111232 High Level Synthesis of Kahn Process Networks(KPN) for Streaming Applications
Authors: Attiya Mahmood, Syed Ali Abbas, Shoab A. Khan
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Streaming Applications usually run in parallel or in series that incrementally transform a stream of input data. It poses a design challenge to break such an application into distinguishable blocks and then to map them into independent hardware processing elements. For this, there is required a generic controller that automatically maps such a stream of data into independent processing elements without any dependencies and manual considerations. In this paper, Kahn Process Networks (KPN) for such streaming applications is designed and developed that will be mapped on MPSoC. This is designed in such a way that there is a generic Cbased compiler that will take the mapping specifications as an input from the user and then it will automate these design constraints and automatically generate the synthesized RTL optimized code for specified application. Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18101231 A Critics Study of Neural Networks Applied to ion-Exchange Process
Authors: John Kabuba, Antoine Mulaba-Bafubiandi, Kim Battle
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This paper presents a critical study about the application of Neural Networks to ion-exchange process. Ionexchange is a complex non-linear process involving many factors influencing the ions uptake mechanisms from the pregnant solution. The following step includes the elution. Published data presents empirical isotherm equations with definite shortcomings resulting in unreliable predictions. Although Neural Network simulation technique encounters a number of disadvantages including its “black box", and a limited ability to explicitly identify possible causal relationships, it has the advantage to implicitly handle complex nonlinear relationships between dependent and independent variables. In the present paper, the Neural Network model based on the back-propagation algorithm Levenberg-Marquardt was developed using a three layer approach with a tangent sigmoid transfer function (tansig) at hidden layer with 11 neurons and linear transfer function (purelin) at out layer. The above mentioned approach has been used to test the effectiveness in simulating ion exchange processes. The modeling results showed that there is an excellent agreement between the experimental data and the predicted values of copper ions removed from aqueous solutions.Keywords: Copper, ion-exchange process, neural networks, simulation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16141230 Hamiltonian Related Properties with and without Faults of the Dual-Cube Interconnection Network and Their Variations
Authors: Shih-Yan Chen, Shin-Shin Kao
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In this paper, a thorough review about dual-cubes, DCn, the related studies and their variations are given. DCn was introduced to be a network which retains the pleasing properties of hypercube Qn but has a much smaller diameter. In fact, it is so constructed that the number of vertices of DCn is equal to the number of vertices of Q2n +1. However, each vertex in DCn is adjacent to n + 1 neighbors and so DCn has (n + 1) × 2^2n edges in total, which is roughly half the number of edges of Q2n+1. In addition, the diameter of any DCn is 2n +2, which is of the same order of that of Q2n+1. For selfcompleteness, basic definitions, construction rules and symbols are provided. We chronicle the results, where eleven significant theorems are presented, and include some open problems at the end.Keywords: Hypercubes, dual-cubes, fault-tolerant hamiltonian property, dual-cube extensive networks, dual-cube-like networks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14491229 Network Analysis in a Natural Perturbed Ecosystem
Authors: Nelson F.F. Ebecken, Gilberto C. Pereira
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The objective of this work is to explicit knowledge on the interactions between the chlorophyll-a and nine meroplankton larvae of epibenthonic fauna. The studied case is the Arraial do Cabo upwelling system, Southeastern of Brazil, which provides different environmental conditions. To assess this information a network approach based in probability estimative was used. Comparisons among the generated graphs are made in the light of different water masses, application of Shannon biodiversity index, and the closeness and betweenness centralities measurements. Our results show the main pattern among different water masses and how the core organisms belonging to the network skeleton are correlated to the main environmental variable. We conclude that the approach of complex networks is a promising tool for environmental diagnostic.
Keywords: Coastal upwelling, Ecological networks, Plankton - interactions, Environmental analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16281228 Forecasting Optimal Production Program Using Profitability Optimization by Genetic Algorithm and Neural Network
Authors: Galal H. Senussi, Muamar Benisa, Sanja Vasin
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In our business field today, one of the most important issues for any enterprises is cost minimization and profit maximization. Second issue is how to develop a strong and capable model that is able to give us desired forecasting of these two issues. Many researches deal with these issues using different methods. In this study, we developed a model for multi-criteria production program optimization, integrated with Artificial Neural Network.
The prediction of the production cost and profit per unit of a product, dealing with two obverse functions at same time can be extremely difficult, especially if there is a great amount of conflict information about production parameters.
Feed-Forward Neural Networks are suitable for generalization, which means that the network will generate a proper output as a result to input it has never seen. Therefore, with small set of examples the network will adjust its weight coefficients so the input will generate a proper output.
This essential characteristic is of the most important abilities enabling this network to be used in variety of problems spreading from engineering to finance etc.
From our results as we will see later, Feed-Forward Neural Networks has a strong ability and capability to map inputs into desired outputs.
Keywords: Project profitability, multi-objective optimization, genetic algorithm, Pareto set, Neural Networks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20481227 Performance Analysis in 5th Generation Massive Multiple-Input-Multiple-Output Systems
Authors: Jihad S. Daba, Jean-Pierre Dubois, Georges El Soury
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Fifth generation wireless networks guarantee significant capacity enhancement to suit more clients and services at higher information rates with better reliability while consuming less power. The deployment of massive multiple-input-multiple-output technology guarantees broadband wireless networks with the use of base station antenna arrays to serve a large number of users on the same frequency and time-slot channels. In this work, we evaluate the performance of massive multiple-input-multiple-output systems (MIMO) systems in 5th generation cellular networks in terms of capacity and bit error rate. Several cases were considered and analyzed to compare the performance of massive MIMO systems while varying the number of antennas at both transmitting and receiving ends. We found that, unlike classical MIMO systems, reducing the number of transmit antennas while increasing the number of antennas at the receiver end provides a better solution to performance enhancement. In addition, enhanced orthogonal frequency division multiplexing and beam division multiple access schemes further improve the performance of massive MIMO systems and make them more reliable.
Keywords: Beam division multiple access, D2D communication, enhanced OFDM, fifth generation broadband, massive MIMO.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7341226 ECG-Based Heartbeat Classification Using Convolutional Neural Networks
Authors: Jacqueline R. T. Alipo-on, Francesca I. F. Escobar, Myles J. T. Tan, Hezerul Abdul Karim, Nouar AlDahoul
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Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases which are considered as one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis on the ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heart beat types. The dataset used in this work is the synthetic MIT-Beth Israel Hospital (MIT-BIH) Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%.
Keywords: Heartbeat classification, convolutional neural network, electrocardiogram signals, ECG signals, generative adversarial networks, long short-term memory, LSTM, ResNet-50.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1631225 Efficient Scheduling Algorithm for QoS Support in High Speed Downlink Packet Access Networks
Authors: MohammadReza HeidariNezhad, Zuriati Ahmad Zukarnain, Nur Izura Udzir, Mohamed Othman
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In this paper, we propose APO, a new packet scheduling scheme with Quality of Service (QoS) support for hybrid of real and non-real time services in HSDPA networks. The APO scheduling algorithm is based on the effective channel anticipation model. In contrast to the traditional schemes, the proposed method is implemented based on a cyclic non-work-conserving discipline. Simulation results indicated that proposed scheme has good capability to maximize the channel usage efficiency in compared to another exist scheduling methods. Simulation results demonstrate the effectiveness of the proposed algorithm.Keywords: Scheduling Algorithm, Quality of Service, HSDPA.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15801224 Trust Enhanced Dynamic Source Routing Protocol for Adhoc Networks
Authors: N. Bhalaji, A. R. Sivaramkrishnan, Sinchan Banerjee, V. Sundar, A. Shanmugam
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Nodes in mobile Ad Hoc Network (MANET) do not rely on a central infrastructure but relay packets originated by other nodes. Mobile ad hoc networks can work properly only if the participating nodes collaborate in routing and forwarding. For individual nodes it might be advantageous not to collaborate, though. In this conceptual paper we propose a new approach based on relationship among the nodes which makes them to cooperate in an Adhoc environment. The trust unit is used to calculate the trust values of each node in the network. The calculated trust values are being used by the relationship estimator to determine the relationship status of nodes. The proposed enhanced protocol was compared with the standard DSR protocol and the results are analyzed using the network simulator-2.Keywords: Reliable Routing, DSR, Grudger, Adhoc network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24961223 On the Impact of Reference Node Placement in Wireless Indoor Positioning Systems
Authors: Supattra Aomumpai, Chutima Prommak
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This paper presents a studyof the impact of reference node locations on the accuracy of the indoor positioning systems. In particular, we analyze the localization accuracy of the RSSI database mapping techniques, deploying on the IEEE 802.15.4 wireless networks. The results show that the locations of the reference nodes used in the positioning systems affect the signal propagation characteristics in the service area. Thisin turn affects the accuracy of the wireless indoor positioning system. We found that suitable location of reference nodes could reduce the positioning error upto 35 %.Keywords: Indoor positioning systems, IEEE 802.15.4 wireless networks, Signal propagation characteristics
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14691222 A Wireless Secure Remote Access Architecture Implementing Role Based Access Control: WiSeR
Authors: E. Tomur, R. Deregozu, T. Genc
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In this study, we propose a network architecture for providing secure access to information resources of enterprise network from remote locations in a wireless fashion. Our proposed architecture offers a very promising solution for organizations which are in need of a secure, flexible and cost-effective remote access methodology. Security of the proposed architecture is based on Virtual Private Network technology and a special role based access control mechanism with location and time constraints. The flexibility mainly comes from the use of Internet as the communication medium and cost-effectiveness is due to the possibility of in-house implementation of the proposed architecture.Keywords: Remote access, wireless networks, security, virtualprivate networks, RBAC.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16841221 High-Power Amplifier Pre-distorter Based on Neural Networks for 5G Satellite Communications
Authors: Abdelhamid Louliej, Younes Jabrane
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Satellites are becoming indispensable assets to fifth-generation (5G) new radio architecture, complementing wireless and terrestrial communication links. The combination of satellites and 5G architecture allows consumers to access all next-generation services anytime, anywhere, including scenarios, like traveling to remote areas (without coverage). Nevertheless, this solution faces several challenges, such as a significant propagation delay, Doppler frequency shift, and high Peak-to-Average Power Ratio (PAPR), causing signal distortion due to the non-linear saturation of the High-Power Amplifier (HPA). To compensate for HPA non-linearity in 5G satellite transmission, an efficient pre-distorter scheme using Neural Networks (NN) is proposed. To assess the proposed NN pre-distorter, two types of HPA were investigated: Travelling Wave Tube Amplifier (TWTA) and Solid-State Power Amplifier (SSPA). The results show that the NN pre-distorter design presents an Error Vector Magnitude (EVM) improvement by 95.26%. Normalized Mean Square Error (NMSE) and Adjacent Channel Power Ratio (ACPR) were reduced by -43,66 dB and 24.56 dBm, respectively. Moreover, the system suffers no degradation of the Bit Error Rate (BER) for TWTA and SSPA amplifiers.
Keywords: Satellites, 5G, Neural Networks, High-Power Amplifier, Travelling Wave Tube Amplifier, Solid-State Power Amplifier, EVM, NMSE, ACPR.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 691220 A Further Study on the 4-Ordered Property of Some Chordal Ring Networks
Authors: Shin-Shin Kao, Hsiu-Chunj Pan
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Given a graph G. A cycle of G is a sequence of vertices of G such that the first and the last vertices are the same. A hamiltonian cycle of G is a cycle containing all vertices of G. The graph G is k-ordered (resp. k-ordered hamiltonian) if for any sequence of k distinct vertices of G, there exists a cycle (resp. hamiltonian cycle) in G containing these k vertices in the specified order. Obviously, any cycle in a graph is 1-ordered, 2-ordered and 3- ordered. Thus the study of any graph being k-ordered (resp. k-ordered hamiltonian) always starts with k = 4. Most studies about this topic work on graphs with no real applications. To our knowledge, the chordal ring families were the first one utilized as the underlying topology in interconnection networks and shown to be 4-ordered. Furthermore, based on our computer experimental results, it was conjectured that some of them are 4-ordered hamiltonian. In this paper, we intend to give some possible directions in proving the conjecture.
Keywords: Hamiltonian cycle, 4-ordered, Chordal rings, 3-regular.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18611219 130 nm CMOS Mixer and VCO for 2.4 GHz Low-power Wireless Personal Area Networks
Authors: Gianluca Cornetta, David J. Santos
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This paper describes a 2.4 GHz passive switch mixer and a 5/2.5 GHz voltage-controlled negative Gm oscillator (VCO) with an inversion-mode MOS varactor. Both circuits are implemented using a 1P8M 0.13 μm process. The switch mixer has an input referred 1 dB compression point of -3.89 dBm and a conversion gain of -0.96 dB when the local oscillator power is +2.5 dBm. The VCO consumes only 1.75 mW, while drawing 1.45 mA from a 1.2 V supply voltage. In order to reduce the passives size, the VCO natural oscillation frequency is 5 GHz. A clocked CMOS divideby- two circuit is used for frequency division and quadrature phase generation. The VCO has a -109 dBc/Hz phase noise at 1 MHz frequency offset and a 2.35-2.5 GHz tuning range (after the frequency division), thus complying with ZigBee requirements.Keywords: Switch Mixers, Varactors, IEEE 802.15.4 (ZigBee), Direct Conversion Receiver, Wireless Sensor Networks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21991218 Face Detection using Gabor Wavelets and Neural Networks
Authors: Hossein Sahoolizadeh, Davood Sarikhanimoghadam, Hamid Dehghani
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This paper proposes new hybrid approaches for face recognition. Gabor wavelets representation of face images is an effective approach for both facial action recognition and face identification. Perform dimensionality reduction and linear discriminate analysis on the down sampled Gabor wavelet faces can increase the discriminate ability. Nearest feature space is extended to various similarity measures. In our experiments, proposed Gabor wavelet faces combined with extended neural net feature space classifier shows very good performance, which can achieve 93 % maximum correct recognition rate on ORL data set without any preprocessing step.Keywords: Face detection, Neural Networks, Multi-layer Perceptron, Gabor wavelets.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21511217 Logic Programming and Artificial Neural Networks in Pharmacological Screening of Schinus Essential Oils
Authors: José Neves, M. Rosário Martins, Fátima Candeias, Diana Ferreira, Sílvia Arantes, Júlio Cruz-Morais, Guida Gomes, Joaquim Macedo, António Abelha, Henrique Vicente
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Some plants of genus Schinus have been used in the folk medicine as topical antiseptic, digestive, purgative, diuretic, analgesic or antidepressant, and also for respiratory and urinary infections. Chemical composition of essential oils of S. molle and S. terebinthifolius had been evaluated and presented high variability according with the part of the plant studied and with the geographic and climatic regions. The pharmacological properties, namely antimicrobial, anti-tumoural and anti-inflammatory activities are conditioned by chemical composition of essential oils. Taking into account the difficulty to infer the pharmacological properties of Schinus essential oils without hard experimental approach, this work will focus on the development of a decision support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks and the respective Degree-of-Confidence that one has on such an occurrence.Keywords: Artificial neuronal networks, essential oils, knowledge representation and reasoning, logic programming, Schinus molle L, Schinus terebinthifolius raddi.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2404