Search results for: complex networks
2974 Performance and Emission Prediction in a Biodiesel Engine Fuelled with Honge Methyl Ester Using RBF Neural Networks
Authors: Shivakumar, G. S. Vijay, P. Srinivas Pai, B. R. Shrinivasa Rao
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In the present study, RBF neural networks were used for predicting the performance and emission parameters of a biodiesel engine. Engine experiments were carried out in a 4 stroke diesel engine using blends of diesel and Honge methyl ester as the fuel. Performance parameters like BTE, BSEC, Tex and emissions from the engine were measured. These experimental results were used for ANN modeling. RBF center initialization was done by random selection and by using Clustered techniques. Network was trained by using fixed and varying widths for the RBF units. It was observed that RBF results were having a good agreement with the experimental results. Networks trained by using clustering technique gave better results than using random selection of centers in terms of reduced MRE and increased prediction accuracy. The average MRE for the performance parameters was 3.25% with the prediction accuracy of 98% and for emissions it was 10.4% with a prediction accuracy of 80%.Keywords: Radial Basis Function networks, emissions, Performance parameters, Fuzzy c means.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17182973 An Overview of Energy Efficient Routing Protocols for Acoustic Sensor Network
Authors: V. P. Dhivya, R. Arthi
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Underwater acoustic network is one of the rapidly growing areas of research and finds different applications for monitoring and collecting various data for environmental studies. The communication among dynamic nodes and high error probability in an acoustic medium forced to maximize energy consumption in Underwater Sensor Networks (USN) than in traditional sensor networks. Developing energy-efficient routing protocol is the fundamental and a curb challenge because all the sensor nodes are powered by batteries, and they cannot be easily replaced in UWSNs. This paper surveys the various recent routing techniques that mainly focus on energy efficiency.
Keywords: Acoustic channels, Energy efficiency, Routing in sensor networks, Underwater Sensor Network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 29822972 Hybrid Algorithm for Frequency Channel Selection in Wi-Fi Networks
Authors: Cesar Hernández, Diego Giral, Ingrid Páez
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This article proposes a hybrid algorithm for spectrum allocation in cognitive radio networks based on the algorithms Analytical Hierarchical Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to improve the performance of the spectrum mobility of secondary users in cognitive radio networks. To calculate the level of performance of the proposed algorithm a comparative analysis between the proposed AHP-TOPSIS, Grey Relational Analysis (GRA) and Multiplicative Exponent Weighting (MEW) algorithm is performed. Four evaluation metrics are used. These metrics are accumulative average of failed handoffs, accumulative average of handoffs performed, accumulative average of transmission bandwidth, and accumulative average of the transmission delay. The results of the comparison show that AHP-TOPSIS Algorithm provides 2.4 times better performance compared to a GRA Algorithm and, 1.5 times better than the MEW Algorithm.Keywords: Cognitive radio, decision making, hybrid algorithm, spectrum handoff, wireless networks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21502971 An Integrative Bayesian Approach to Supporting the Prediction of Protein-Protein Interactions: A Case Study in Human Heart Failure
Authors: Fiona Browne, Huiru Zheng, Haiying Wang, Francisco Azuaje
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Recent years have seen a growing trend towards the integration of multiple information sources to support large-scale prediction of protein-protein interaction (PPI) networks in model organisms. Despite advances in computational approaches, the combination of multiple “omic" datasets representing the same type of data, e.g. different gene expression datasets, has not been rigorously studied. Furthermore, there is a need to further investigate the inference capability of powerful approaches, such as fullyconnected Bayesian networks, in the context of the prediction of PPI networks. This paper addresses these limitations by proposing a Bayesian approach to integrate multiple datasets, some of which encode the same type of “omic" data to support the identification of PPI networks. The case study reported involved the combination of three gene expression datasets relevant to human heart failure (HF). In comparison with two traditional methods, Naive Bayesian and maximum likelihood ratio approaches, the proposed technique can accurately identify known PPI and can be applied to infer potentially novel interactions.Keywords: Bayesian network, Classification, Data integration, Protein interaction networks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16082970 The Usage of Social Networks in Educational Context
Authors: Sacide Güzin Mazman, Yasemin Koçak Usluel
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Possible advantages of technology in educational context required the defining boundaries of formal and informal learning. Increasing opportunity to ubiquitous learning by technological support has revealed a question of how to discover the potential of individuals in the spontaneous environments such as social networks. This seems to be related with the question of what purposes in social networks have been being used? Social networks provide various advantages in educational context as collaboration, knowledge sharing, common interests, active participation and reflective thinking. As a consequence of these, the purpose of this study is composed of proposing a new model that could determine factors which effect adoption of social network applications for usage in educational context. While developing a model proposal, the existing adoption and diffusion models have been reviewed and they are thought to be suitable on handling an original perspective instead of using completely other diffusion or acceptance models because of different natures of education from other organizations. In the proposed model; social factors, perceived ease of use, perceived usefulness and innovativeness are determined four direct constructs that effect adoption process. Facilitating conditions, image, subjective norms and community identity are incorporated to model as antecedents of these direct four constructs.Keywords: Adoption of innovation, educational context, social networks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 38652969 Secured Session Based Profile Caching for E-Learning Systems Using WiMAX Networks
Authors: R. Chithra, B. Kalaavathi
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E-Learning enables the users to learn at anywhere at any time. In E-Learning systems, authenticating the E-Learning user has security issues. The usage of appropriate communication networks for providing the internet connectivity for E-learning is another challenge. WiMAX networks provide Broadband Wireless Access through the Multicast Broadcast Service so these networks can be most suitable for E-Learning applications. The authentication of E-Learning user is vulnerable to session hijacking problems. The repeated authentication of users can be done to overcome these issues. In this paper, session based Profile Caching Authentication is proposed. In this scheme, the credentials of E-Learning users can be cached at authentication server during the initial authentication through the appropriate subscriber station. The proposed cache based authentication scheme performs fast authentication by using cached user profile. Thus, the proposed authentication protocol reduces the delay in repeated authentication to enhance the security in ELearning.Keywords: Authentication, E-Learning, WiMAX, Security, Profile caching.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15542968 The Relations between the Fractal Properties of the River Networks and the River Flow Time Series
Authors: M. H. Fattahi, H. Jahangiri
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All the geophysical phenomena including river networks and flow time series are fractal events inherently and fractal patterns can be investigated through their behaviors. A non-linear system like a river basin can well be analyzed by a non-linear measure such as the fractal analysis. A bilateral study is held on the fractal properties of the river network and the river flow time series. A moving window technique is utilized to scan the fractal properties of them. Results depict both events follow the same strategy regarding to the fractal properties. Both the river network and the time series fractal dimension tend to saturate in a distinct value.Keywords: river flow time series, fractal, river networks
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16772967 Neural Networks for Distinguishing the Performance of Two Hip Joint Implants on the Basis of Hip Implant Side and Ground Reaction Force
Authors: L. Parisi
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In this research work, neural networks were applied to classify two types of hip joint implants based on the relative hip joint implant side speed and three components of each ground reaction force. The condition of walking gait at normal velocity was used and carried out with each of the two hip joint implants assessed. Ground reaction forces’ kinetic temporal changes were considered in the first approach followed but discarded in the second one. Ground reaction force components were obtained from eighteen patients under such gait condition, half of which had a hip implant type I-II, whilst the other half had the hip implant, defined as type III by Orthoload®. After pre-processing raw gait kinetic data and selecting the time frames needed for the analysis, the ground reaction force components were used to train a MLP neural network, which learnt to distinguish the two hip joint implants in the abovementioned condition. Further to training, unknown hip implant side and ground reaction force components were presented to the neural networks, which assigned those features into the right class with a reasonably high accuracy for the hip implant type I-II and the type III. The results suggest that neural networks could be successfully applied in the performance assessment of hip joint implants.
Keywords: Kinemic gait data, Neural networks, Hip joint implant, Hip arthroplasty, Rehabilitation Engineering.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17882966 Coverage and Connectivity Problem in Sensor Networks
Authors: Meenakshi Bansal, Iqbal Singh, Parvinder S. Sandhu
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In over deployed sensor networks, one approach to Conserve energy is to keep only a small subset of sensors active at Any instant. For the coverage problems, the monitoring area in a set of points that require sensing, called demand points, and consider that the node coverage area is a circle of range R, where R is the sensing range, If the Distance between a demand point and a sensor node is less than R, the node is able to cover this point. We consider a wireless sensor network consisting of a set of sensors deployed randomly. A point in the monitored area is covered if it is within the sensing range of a sensor. In some applications, when the network is sufficiently dense, area coverage can be approximated by guaranteeing point coverage. In this case, all the points of wireless devices could be used to represent the whole area, and the working sensors are supposed to cover all the sensors. We also introduce Hybrid Algorithm and challenges related to coverage in sensor networks.Keywords: Wireless sensor networks, network coverage, Energy conservation, Hybrid Algorithms.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17082965 Clustering Approach to Unveiling Relationships between Gene Regulatory Networks
Authors: Hiba Hasan, Khalid Raza
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Reverse engineering of genetic regulatory network involves the modeling of the given gene expression data into a form of the network. Computationally it is possible to have the relationships between genes, so called gene regulatory networks (GRNs), that can help to find the genomics and proteomics based diagnostic approach for any disease. In this paper, clustering based method has been used to reconstruct genetic regulatory network from time series gene expression data. Supercoiled data set from Escherichia coli has been taken to demonstrate the proposed method.
Keywords: Gene expression, gene regulatory networks (GRNs), clustering, data preprocessing, network visualization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21392964 High Impedance Fault Detection using LVQ Neural Networks
Authors: Abhishek Bansal, G. N. Pillai
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This paper presents a new method to detect high impedance faults in radial distribution systems. Magnitudes of third and fifth harmonic components of voltages and currents are used as a feature vector for fault discrimination. The proposed methodology uses a learning vector quantization (LVQ) neural network as a classifier for identifying high impedance arc-type faults. The network learns from the data obtained from simulation of a simple radial system under different fault and system conditions. Compared to a feed-forward neural network, a properly tuned LVQ network gives quicker response.Keywords: Fault identification, distribution networks, high impedance arc-faults, feature vector, LVQ networks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22042963 Performance Analysis of Heterogeneous Cellular Networks with Multiple Connectivity
Authors: Sungkyung Kim, Jee-Hyeon Na, Dong-Seung Kwon
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Future mobile networks following 5th generation will be characterized by one thousand times higher gains in capacity; connections for at least one hundred billion devices; user experience capable of extremely low latency and response times. To be close to the capacity requirements and higher reliability, advanced technologies have been studied, such as multiple connectivity, small cell enhancement, heterogeneous networking, and advanced interference and mobility management. This paper is focused on the multiple connectivity in heterogeneous cellular networks. We investigate the performance of coverage and user throughput in several deployment scenarios. Using the stochastic geometry approach, the SINR distributions and the coverage probabilities are derived in case of dual connection. Also, to compare the user throughput enhancement among the deployment scenarios, we calculate the spectral efficiency and discuss our results.
Keywords: Heterogeneous networks, multiple connectivity, small cell enhancement, stochastic geometry.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19552962 Enabling Integration across Heterogeneous Care Networks
Authors: Federico Cabitza, Marco P. Locatelli, Marcello Sarini, Carla Simone
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The paper shows how the CASMAS modeling language, and its associated pervasive computing architecture, can be used to facilitate continuity of care by providing members of patientcentered communities of care with a support to cooperation and knowledge sharing through the usage of electronic documents and digital devices. We consider a scenario of clearly fragmented care to show how proper mechanisms can be defined to facilitate a better integration of practices and information across heterogeneous care networks. The scenario is declined in terms of architectural components and cooperation-oriented mechanisms that make the support reactive to the evolution of the context where these communities operate.Keywords: Pervasive Computing, Communities of Care, HeterogeneousCare Networks, Multi-Agent System.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13502961 Transmitter Macrodiversity in Multihopping- SFN Based Algorithm for Improved Node Reachability and Robust Routing
Authors: Magnus Eriksson, Arif Mahmud
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A novel idea presented in this paper is to combine multihop routing with single-frequency networks (SFNs) for a broadcasting scenario. An SFN is a set of multiple nodes that transmit the same data simultaneously, resulting in transmitter macrodiversity. Two of the most important performance factors of multihop networks, node reachability and routing robustness, are analyzed. Simulation results show that our proposed SFN-D routing algorithm improves the node reachability by 37 percentage points as compared to non-SFN multihop routing. It shows a diversity gain of 3.7 dB, meaning that 3.7 dB lower transmission powers are required for the same reachability. Even better results are possible for larger networks. If an important node becomes inactive, this algorithm can find new routes that a non-SFN scheme would not be able to find. Thus, two of the major problems in multihopping are addressed; achieving robust routing as well as improving node reachability or reducing transmission power.Keywords: OFDM, single-frequency networks (SFN), DSFN, MANET; multihop routing, transmitter macrodiversity, broadcasting.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19162960 Deep Learning Based, End-to-End Metaphor Detection in Greek with Recurrent and Convolutional Neural Networks
Authors: Konstantinos Perifanos, Eirini Florou, Dionysis Goutsos
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This paper presents and benchmarks a number of end-to-end Deep Learning based models for metaphor detection in Greek. We combine Convolutional Neural Networks and Recurrent Neural Networks with representation learning to bear on the metaphor detection problem for the Greek language. The models presented achieve exceptional accuracy scores, significantly improving the previous state-of-the-art results, which had already achieved accuracy 0.82. Furthermore, no special preprocessing, feature engineering or linguistic knowledge is used in this work. The methods presented achieve accuracy of 0.92 and F-score 0.92 with Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory networks (LSTMs). Comparable results of 0.91 accuracy and 0.91 F-score are also achieved with bidirectional Gated Recurrent Units (GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The models are trained and evaluated only on the basis of training tuples, the related sentences and their labels. The outcome is a state-of-the-art collection of metaphor detection models, trained on limited labelled resources, which can be extended to other languages and similar tasks.Keywords: Metaphor detection, deep learning, representation learning, embeddings.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 5292959 Some Remarkable Properties of a Hopfield Neural Network with Time Delay
Authors: Kelvin Rozier, Vladimir E. Bondarenko
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It is known that an analog Hopfield neural network with time delay can generate the outputs which are similar to the human electroencephalogram. To gain deeper insights into the mechanisms of rhythm generation by the Hopfield neural networks and to study the effects of noise on their activities, we investigated the behaviors of the networks with symmetric and asymmetric interneuron connections. The neural network under the study consists of 10 identical neurons. For symmetric (fully connected) networks all interneuron connections aij = +1; the interneuron connections for asymmetric networks form an upper triangular matrix with non-zero entries aij = +1. The behavior of the network is described by 10 differential equations, which are solved numerically. The results of simulations demonstrate some remarkable properties of a Hopfield neural network, such as linear growth of outputs, dependence of synchronization properties on the connection type, huge amplification of oscillation by the external uniform noise, and the capability of the neural network to transform one type of noise to another.Keywords: Chaos, Hopfield neural network, noise, synchronization
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18772958 A Utilitarian Approach to Modeling Information Flows in Social Networks
Authors: Usha Sridhar, Sridhar Mandyam
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We propose a multi-agent based utilitarian approach to model and understand information flows in social networks that lead to Pareto optimal informational exchanges. We model the individual expected utility function of the agents to reflect the net value of information received. We show how this model, adapted from a theorem by Karl Borch dealing with an actuarial Risk Exchange concept in the Insurance industry, can be used for social network analysis. We develop a utilitarian framework that allows us to interpret Pareto optimal exchanges of value as potential information flows, while achieving a maximization of a sum of expected utilities of information of the group of agents. We examine some interesting conditions on the utility function under which the flows are optimal. We illustrate the promise of this new approach to attach economic value to information in networks with a synthetic example.Keywords: Borch's Theorem , Economic value of information, Information Exchange, Pareto Optimal Solution, Social Networks, Utility Functions
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14962957 Layered Multiple Description Coding For Robust Video Transmission Over Wireless Ad-Hoc Networks
Authors: Joohee Kim
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This paper presents a video transmission system using layered multiple description (coding (MDC) and multi-path transport for reliable video communications in wireless ad-hoc networks. The proposed MDC extends a quality-scalable H.264/AVC video coding algorithm to generate two independent descriptions. The two descriptions are transmitted over different paths to a receiver in order to alleviate the effect of unstable channel conditions of wireless adhoc networks. If one description is lost due to transmission erros, then the correctly received description is used to estimate the lost information of the corrupted description. The proposed MD coder maintains an adequate video quality as long as both description are not simultaneously lost. Simulation results show that the proposed MD coding combined with multi-path transport system is largely immune to packet losses, and therefore, can be a promising solution for robust video communications over wireless ad-hoc networks.Keywords: Multiple description coding, wireless video streaming, rate control.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14352956 Discrimination of Modes of Double- and Single-Negative Grounded Slab
Authors: R. Borghol, T. Aguili
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In this paper, we investigate theoretically the waves propagation in a lossless double-negative grounded slab (DNG). This study is performed by the Transverse Resonance Method (TRM). The proper or improper nature of real and complex modes is observed. They are highly dependent on metamaterial parameters, i.e. ɛr-negative, µr-negative, or both. Numerical results provided that only the proper complex modes (i.e., leaky modes) exist in DNG slab, and only the improper complex modes exist in single-negative grounded slab.
Keywords: Double-negative grounded slab, real and complex modes, single-negative grounded slab, transverse resonance method.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9542955 Comparative Analysis of Geographical Routing Protocol in Wireless Sensor Networks
Authors: Rahul Malhotra
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The field of wireless sensor networks (WSN) engages a lot of associates in the research community as an interdisciplinary field of interest. This type of network is inexpensive, multifunctionally attributable to advances in micro-electromechanical systems and conjointly the explosion and expansion of wireless communications. A mobile ad hoc network is a wireless network without fastened infrastructure or federal management. Due to the infrastructure-less mode of operation, mobile ad-hoc networks are gaining quality. During this work, we have performed an efficient performance study of the two major routing protocols: Ad hoc On-Demand Distance Vector Routing (AODV) and Dynamic Source Routing (DSR) protocols. We have used an accurate simulation model supported NS2 for this purpose. Our simulation results showed that AODV mitigates the drawbacks of the DSDV and provides better performance as compared to DSDV.
Keywords: Routing protocols, mobility, Mobile Ad-hoc Networks, Ad-hoc On-demand Distance Vector, Dynamic Source Routing, Destination Sequence Distance Vector, Quality of Service.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6992954 Use of Integrated Knowledge Networks to Increase Innovation in Nanotechnology Research and Development
Authors: R. Byler
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Innovation, particularly in technology development, is a crucial aspect of nanotechnology R&D and, although several approaches to effective innovation management exist, organizational structures that promote knowledge exchange have been found to be most effect in supporting new and emerging technologies. This paper discusses Integrated Knowledge Networks (IKNs) and evaluates its use within nanotechnology R&D to increase technology innovation. Specifically, this paper reviews the role of IKNs in bolstering national and international nanotechnology development and in enhancing nanotechnology innovation. Both physical and virtual IKNs, particularly IT-based network platforms for community-based innovation, offer strategies for enhanced technology innovation, interdisciplinary cooperation, and enterprise development. Effectively creating and managing technology R&D networks can facilitate successful knowledge exchange, enhanced innovation, commercialization, and technology transfer. As such, IKNs are crucial to technology development processes and, thus, in increasing the quality and access to new, innovative nanoscience and technologies worldwide.Keywords: Community-based innovation, integrated knowledge networks, nanotechnology, technology innovation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8902953 Handling Complexity of a Complex System Design: Paradigm, Formalism and Transformations
Authors: Hycham Aboutaleb, Bruno Monsuez
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Current systems complexity has reached a degree that requires addressing conception and design issues while taking into account environmental, operational, social, legal and financial aspects. Therefore, one of the main challenges is the way complex systems are specified and designed. The exponential growing effort, cost and time investment of complex systems in modeling phase emphasize the need for a paradigm, a framework and an environment to handle the system model complexity. For that, it is necessary to understand the expectations of the human user of the model and his limits. This paper presents a generic framework for designing complex systems, highlights the requirements a system model needs to fulfill to meet human user expectations, and suggests a graphbased formalism for modeling complex systems. Finally, a set of transformations are defined to handle the model complexity.Keywords: Higraph-based, formalism, system engineering paradigm, modeling requirements, graph-based transformations.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16312952 Efficient Broadcasting in Wireless Sensor Networks
Authors: Min Kyung An, Hyuk Cho
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In this paper, we study the Minimum Latency Broadcast Scheduling (MLBS) problem in wireless sensor networks (WSNs). The main issue of the MLBS problem is to compute schedules with the minimum number of timeslots such that a base station can broadcast data to all other sensor nodes with no collisions. Unlike existing works that utilize the traditional omni-directional WSNs, we target the directional WSNs where nodes can collaboratively determine and orientate their antenna directions. We first develop a 7-approximation algorithm, adopting directional WSNs. Our ratio is currently the best, to the best of our knowledge. We then validate the performance of the proposed algorithm through simulation.Keywords: Broadcast, collision-free, directional antenna, approximation, wireless sensor networks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11422951 Periodic Solutions of Recurrent Neural Networks with Distributed Delays and Impulses on Time Scales
Authors: Yaping Ren, Yongkun Li
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In this paper, by using the continuation theorem of coincidence degree theory, M-matrix theory and constructing some suitable Lyapunov functions, some sufficient conditions are obtained for the existence and global exponential stability of periodic solutions of recurrent neural networks with distributed delays and impulses on time scales. Without assuming the boundedness of the activation functions gj, hj , these results are less restrictive than those given in the earlier references.
Keywords: Recurrent neural networks, global exponential stability, periodic solutions, distributed delays, impulses, time scales.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15792950 Unknown Environment Representation for Mobile Robot Using Spiking Neural Networks
Authors: Amir Reza Saffari Azar Alamdari
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In this paper, a model of self-organizing spiking neural networks is introduced and applied to mobile robot environment representation and path planning problem. A network of spike-response-model neurons with a recurrent architecture is used to create robot-s internal representation from surrounding environment. The overall activity of network simulates a self-organizing system with unsupervised learning. A modified A* algorithm is used to find the best path using this internal representation between starting and goal points. This method can be used with good performance for both known and unknown environments.
Keywords: Mobile Robot, Path Planning, Self-organization, Spiking Neural Networks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14812949 Estimation of Broadcast Probability in Wireless Adhoc Networks
Authors: Bharadwaj Kadiyala, Sunitha V
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Most routing protocols (DSR, AODV etc.) that have been designed for wireless adhoc networks incorporate the broadcasting operation in their route discovery scheme. Probabilistic broadcasting techniques have been developed to optimize the broadcast operation which is otherwise very expensive in terms of the redundancy and the traffic it generates. In this paper we have explored percolation theory to gain a different perspective on probabilistic broadcasting schemes which have been actively researched in the recent years. This theory has helped us estimate the value of broadcast probability in a wireless adhoc network as a function of the size of the network. We also show that, operating at those optimal values of broadcast probability there is at least 25-30% reduction in packet regeneration during successful broadcasting.Keywords: Crossover length, Percolation, Probabilistic broadcast, Wireless adhoc networks
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15812948 Mixtures of Monotone Networks for Prediction
Authors: Marina Velikova, Hennie Daniels, Ad Feelders
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In many data mining applications, it is a priori known that the target function should satisfy certain constraints imposed by, for example, economic theory or a human-decision maker. In this paper we consider partially monotone prediction problems, where the target variable depends monotonically on some of the input variables but not on all. We propose a novel method to construct prediction models, where monotone dependences with respect to some of the input variables are preserved by virtue of construction. Our method belongs to the class of mixture models. The basic idea is to convolute monotone neural networks with weight (kernel) functions to make predictions. By using simulation and real case studies, we demonstrate the application of our method. To obtain sound assessment for the performance of our approach, we use standard neural networks with weight decay and partially monotone linear models as benchmark methods for comparison. The results show that our approach outperforms partially monotone linear models in terms of accuracy. Furthermore, the incorporation of partial monotonicity constraints not only leads to models that are in accordance with the decision maker's expertise, but also reduces considerably the model variance in comparison to standard neural networks with weight decay.Keywords: mixture models, monotone neural networks, partially monotone models, partially monotone problems.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12362947 Optical Wireless Sensor Networks Based on VLC with PLC-Ethernet Interface
Authors: Heejin Lee, Yeonjoo Kim, Kyungrak Sohn
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We present a white LED-based optical wireless communication systems for indoor ubiquitous sensor networks. Each sensor node could access to the server through the PLC (Power Line Communication)-Ethernet interface. The proposed system offers a full-duplex wireless link by using different wavelengths to reduce the inter-symbol interference between uplink and downlink. Through the 1-to-n optical wireless sensor network and PLC modem, the mobile terminals send a temperature data to server. The data transmission speed and distance are 115.2kbps and about 60cm, respectively.Keywords: Visible light communications, LED lighting, powerline communications, ubiquitous sensor networks, full-duplex links
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 37422946 Almost Periodicity in a Harvesting Lotka-Volterra Recurrent Neural Networks with Time-Varying Delays
Authors: Yongzhi Liao
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By using the theory of exponential dichotomy and Banach fixed point theorem, this paper is concerned with the problem of the existence and uniqueness of positive almost periodic solution in a delayed Lotka-Volterra recurrent neural networks with harvesting terms. To a certain extent, our work in this paper corrects some result in recent years. Finally, an example is given to illustrate the feasibility and effectiveness of the main result.
Keywords: positive almost periodic solution, Lotka-Volterra, neural networks, Banach fixed point theorem, harvesting
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16082945 An Improved QRS Complex Detection for Online Medical Diagnosis
Authors: I. L. Ahmad, M. Mohamed, N. A. Ab. Ghani
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This paper presents the work of signal discrimination specifically for Electrocardiogram (ECG) waveform. ECG signal is comprised of P, QRS, and T waves in each normal heart beat to describe the pattern of heart rhythms corresponds to a specific individual. Further medical diagnosis could be done to determine any heart related disease using ECG information. The emphasis on QRS Complex classification is further discussed to illustrate the importance of it. Pan-Tompkins Algorithm, a widely known technique has been adapted to realize the QRS Complex classification process. There are eight steps involved namely sampling, normalization, low pass filter, high pass filter (build a band pass filter), derivation, squaring, averaging and lastly is the QRS detection. The simulation results obtained is represented in a Graphical User Interface (GUI) developed using MATLAB.Keywords: ECG, Pan Tompkins Algorithm, QRS Complex, Simulation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2563