Search results for: home area network
4637 Topology Influence on TCP Congestion Control Performance in Multi-hop Ad Hoc Wireless
Authors: Haniza N., Md Khambari, M. N, Shahrin S., Adib M.Monzer Habbal, Suhaidi Hassan
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Wireless ad hoc nodes are freely and dynamically self-organize in communicating with others. Each node can act as host or router. However it actually depends on the capability of nodes in terms of its current power level, signal strength, number of hops, routing protocol, interference and others. In this research, a study was conducted to observe the effect of hops count over different network topologies that contribute to TCP Congestion Control performance degradation. To achieve this objective, a simulation using NS-2 with different topologies have been evaluated. The comparative analysis has been discussed based on standard observation metrics: throughput, delay and packet loss ratio. As a result, there is a relationship between types of topology and hops counts towards the performance of ad hoc network. In future, the extension study will be carried out to investigate the effect of different error rate and background traffic over same topologies.Keywords: NS-2, network topology, network performance, multi-hops
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15514636 Remaining Useful Life Prediction Using Elliptical Basis Function Network and Markov Chain
Authors: Yi Yu, Lin Ma, Yong Sun, Yuantong Gu
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This paper presents a novel method for remaining useful life prediction using the Elliptical Basis Function (EBF) network and a Markov chain. The EBF structure is trained by a modified Expectation-Maximization (EM) algorithm in order to take into account the missing covariate set. No explicit extrapolation is needed for internal covariates while a Markov chain is constructed to represent the evolution of external covariates in the study. The estimated external and the unknown internal covariates constitute an incomplete covariate set which are then used and analyzed by the EBF network to provide survival information of the asset. It is shown in the case study that the method slightly underestimates the remaining useful life of an asset which is a desirable result for early maintenance decision and resource planning.Keywords: Elliptical Basis Function Network, Markov Chain, Missing Covariates, Remaining Useful Life
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16484635 An Approach to Secure Mobile Agent Communication in Multi-Agent Systems
Authors: Olumide Simeon Ogunnusi, Shukor Abd Razak, Michael Kolade Adu
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Inter-agent communication manager facilitates communication among mobile agents via message passing mechanism. Until now, all Foundation for Intelligent Physical Agents (FIPA) compliant agent systems are capable of exchanging messages following the standard format of sending and receiving messages. Previous works tend to secure messages to be exchanged among a community of collaborative agents commissioned to perform specific tasks using cryptosystems. However, the approach is characterized by computational complexity due to the encryption and decryption processes required at the two ends. The proposed approach to secure agent communication allows only agents that are created by the host agent server to communicate via the agent communication channel provided by the host agent platform. These agents are assumed to be harmless. Therefore, to secure communication of legitimate agents from intrusion by external agents, a 2-phase policy enforcement system was developed. The first phase constrains the external agent to run only on the network server while the second phase confines the activities of the external agent to its execution environment. To implement the proposed policy, a controller agent was charged with the task of screening any external agent entering the local area network and preventing it from migrating to the agent execution host where the legitimate agents are running. On arrival of the external agent at the host network server, an introspector agent was charged to monitor and restrain its activities. This approach secures legitimate agent communication from Man-in-the Middle and Replay attacks.
Keywords: Agent communication, introspective agent, isolation of agent, policy enforcement system.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6294634 Location Based Clustering in Wireless Sensor Networks
Authors: Ashok Kumar, Narottam Chand, Vinod Kumar
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Due to the limited energy resources, energy efficient operation of sensor node is a key issue in wireless sensor networks. Clustering is an effective method to prolong the lifetime of energy constrained wireless sensor network. However, clustering in wireless sensor network faces several challenges such as selection of an optimal group of sensor nodes as cluster, optimum selection of cluster head, energy balanced optimal strategy for rotating the role of cluster head in a cluster, maintaining intra and inter cluster connectivity and optimal data routing in the network. In this paper, we propose a protocol supporting an energy efficient clustering, cluster head selection/rotation and data routing method to prolong the lifetime of sensor network. Simulation results demonstrate that the proposed protocol prolongs network lifetime due to the use of efficient clustering, cluster head selection/rotation and data routing.
Keywords: Wireless sensor networks, clustering, energy efficient, localization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 26744633 Effect of Clustering on Energy Efficiency and Network Lifetime in Wireless Sensor Networks
Authors: Prakash G L, Chaitra K Meti, Poojitha K, Divya R.K.
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Wireless Sensor Network is Multi hop Self-configuring Wireless Network consisting of sensor nodes. The deployment of wireless sensor networks in many application areas, e.g., aggregation services, requires self-organization of the network nodes into clusters. Efficient way to enhance the lifetime of the system is to partition the network into distinct clusters with a high energy node as cluster head. The different methods of node clustering techniques have appeared in the literature, and roughly fall into two families; those based on the construction of a dominating set and those which are based solely on energy considerations. Energy optimized cluster formation for a set of randomly scattered wireless sensors is presented. Sensors within a cluster are expected to be communicating with cluster head only. The energy constraint and limited computing resources of the sensor nodes present the major challenges in gathering the data. In this paper we propose a framework to study how partially correlated data affect the performance of clustering algorithms. The total energy consumption and network lifetime can be analyzed by combining random geometry techniques and rate distortion theory. We also present the relation between compression distortion and data correlation.Keywords: Clusters, multi hop, random geometry, rate distortion.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16254632 The Relations between Seismic Results and Groundwater near the Gokpinar Damp Area, Denizli, Turkey
Authors: Mahmud Gungor, Ali Aydin, Erdal Akyol, Suat Tasdelen
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The understanding of geotechnical characteristics of near-surface material and the effects of the groundwater is very important problem in such as site studies. For showing the relations between seismic data and groundwater, we selected about 25 km2 as the study area. It has been presented which is a detailed work of seismic data and groundwater depths of Gokpinar Damp area. Seismic waves velocity (Vp and Vs) are very important parameters showing the soil properties. The seismic records were used the method of the multichannel analysis of surface waves near area of Gokpinar Damp area. Sixty sites in this area have been investigated with survey lines about 60 m in length. MASW (Multichannel analysis of surface wave) method has been used to generate onedimensional shear wave velocity profile at locations. These shear wave velocities are used to estimate equivalent shear wave velocity in the study area at every 2 and 5 m intervals up to a depth of 45 m. Levels of equivalent shear wave velocity of soil are used the classified of the study area. After the results of the study, it must be considered as components of urban planning and building design of Gokpinar Damp area, Denizli and the application and use of these results should be required and enforced by municipal authorities.
Keywords: Seismic data, Gokpinar Damp, urban planning, Denizli.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23464631 Measuring Heterogeneous Traffic Density
Authors: V. Thamizh Arasan, G. Dhivya
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Traffic Density provides an indication of the level of service being provided to the road users. Hence, there is a need to study the traffic flow characteristics with specific reference to density in detail. When the length and speed of the vehicles in a traffic stream vary significantly, the concept of occupancy, rather than density, is more appropriate to describe traffic concentration. When the concept of occupancy is applied to heterogeneous traffic condition, it is necessary to consider the area of the road space and the area of the vehicles as the bases. Hence, a new concept named, 'area-occupancy' is proposed here. It has been found that the estimated area-occupancy gives consistent values irrespective of change in traffic composition.Keywords: Density Measurement, Heterogeneity, Occupancy, Traffic Flow.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 32174630 Applications of Artificial Neural Network to Building Statistical Models for Qualifying and Indexing Radiation Treatment Plans
Authors: Pei-Ju Chao, Tsair-Fwu Lee, Wei-Luen Huang, Long-Chang Chen, Te-Jen Su, Wen-Ping Chen
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The main goal in this paper is to quantify the quality of different techniques for radiation treatment plans, a back-propagation artificial neural network (ANN) combined with biomedicine theory was used to model thirteen dosimetric parameters and to calculate two dosimetric indices. The correlations between dosimetric indices and quality of life were extracted as the features and used in the ANN model to make decisions in the clinic. The simulation results show that a trained multilayer back-propagation neural network model can help a doctor accept or reject a plan efficiently. In addition, the models are flexible and whenever a new treatment technique enters the market, the feature variables simply need to be imported and the model re-trained for it to be ready for use.Keywords: neural network, dosimetric index, radiation treatment, tumor
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16724629 Unveiling the Mathematical Essence of Machine Learning: A Comprehensive Exploration
Authors: Randhir Singh Baghel
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In this study, the fundamental ideas guiding the dynamic area of machine learning—where models thrive and algorithms change over time—are rooted in an innate mathematical link. This study explores the fundamental ideas that drive the development of intelligent systems, providing light on the mutually beneficial link between mathematics and machine learning.
Keywords: Machine Learning, deep learning, Neural Network, optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1134628 Juvenile Delinquency of Senior High School Students in Surabaya, Indonesia
Authors: Herdina Indrijati
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This research aims to describe teenager delinquency behavior (Juvenile Delinquency) of senior high school students in Surabaya, Indonesia. Juvenile Delinquency is a broad range of behaviors start from socially unacceptable behavior (overreact in school), violation (escape from home) to crimes (like stealing). This research uses quantitative descriptive method using 498 students who come from 8 different schools in Surabaya as subjects. Juvenile Delinquency behavior form questionnaire has been completed by subjects and was used to measure and describe the behavior. The result of this research is presented in statistic descriptive forms. Result shows that 169 subjects skip school, 55 subjects get out of home without parent’s permission, 110 subjects engage in smoking behavior, 74 subjects damage other people properties, 32 subjects steal, 16 subjects exploit others and 7 subjects engage in drug abuse. Frequency of the top five mentioned behavior are 1-10 times. It is also found that subject’s peers are most likely to be the victim of Juvenile Delinquency. The reasons teenagers engage in Juvenile Delinquency include (1) feeling tired, bored or lazy – that contributes to their skip school behavior (2) Having a lot of problem with parents - contrives them to run away from home, (3) accidentally damage other people’s properties, (4) financial problems – force them to steal and exploit, (5) feeling like having a lot of life problems – that makes them do drugs (6) trying smoking for experience.Keywords: Juvenile delinquency, senior high school, student.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15754627 A Methodology for Definition of Road Networks in Rural Areas of Nepal
Authors: J. K. Shrestha, A. Benta, R. B. Lopes, N. Lopes
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This work provides a practical method for the development of rural road networks in rural areas of developing countries. The proposed methodology enables to determine obligatory points in the rural road network maximizing the number of settlements that have access to basic services within a given maximum distance. The proposed methodology is simple and practical, hence, highly applicable to real-world scenarios, as demonstrated in the definition of the road network for the rural areas of Nepal.Keywords: Minimum spanning tree, nodal points, rural road network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 28624626 Hypergraph Models of Metabolism
Authors: Nicole Pearcy, Jonathan J. Crofts, Nadia Chuzhanova
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In this paper, we employ a directed hypergraph model to investigate the extent to which environmental variability influences the set of available biochemical reactions within a living cell. Such an approach avoids the limitations of the usual complex network formalism by allowing for the multilateral relationships (i.e. connections involving more than two nodes) that naturally occur within many biological processes. More specifically, we extend the concept of network reciprocity to complex hyper-networks, thus enabling us to characterise a network in terms of the existence of mutual hyper-connections, which may be considered a proxy for metabolic network complexity. To demonstrate these ideas, we study 115 metabolic hyper-networks of bacteria, each of which can be classified into one of 6 increasingly varied habitats. In particular, we found that reciprocity increases significantly with increased environmental variability, supporting the view that organism adaptability leads to increased complexities in the resultant biochemical networks.
Keywords: Complexity, hypergraphs, reciprocity, metabolism.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24344625 Performance Evaluation of Neural Network Prediction for Data Prefetching in Embedded Applications
Authors: Sofien Chtourou, Mohamed Chtourou, Omar Hammami
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Embedded systems need to respect stringent real time constraints. Various hardware components included in such systems such as cache memories exhibit variability and therefore affect execution time. Indeed, a cache memory access from an embedded microprocessor might result in a cache hit where the data is available or a cache miss and the data need to be fetched with an additional delay from an external memory. It is therefore highly desirable to predict future memory accesses during execution in order to appropriately prefetch data without incurring delays. In this paper, we evaluate the potential of several artificial neural networks for the prediction of instruction memory addresses. Neural network have the potential to tackle the nonlinear behavior observed in memory accesses during program execution and their demonstrated numerous hardware implementation emphasize this choice over traditional forecasting techniques for their inclusion in embedded systems. However, embedded applications execute millions of instructions and therefore millions of addresses to be predicted. This very challenging problem of neural network based prediction of large time series is approached in this paper by evaluating various neural network architectures based on the recurrent neural network paradigm with pre-processing based on the Self Organizing Map (SOM) classification technique.Keywords: Address, data set, memory, prediction, recurrentneural network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16624624 Avoiding Pin Ball Routing Problem in Network Mobility Hand-Off Management
Authors: M. Dinakaran, P. Balasubramanie
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With the demand of mobility by users, wireless technologies have become the hotspot developing arena. Internet Engineering Task Force (IETF) working group has developed Mobile IP to support node mobility. The concept of node mobility indicates that in spite of the movement of the node, it is still connected to the internet and all the data transactions are preserved. It provides location-independent access to Internet. After the incorporation of host mobility, network mobility has undergone intense research. There are several intricacies faced in the real world implementation of network mobility significantly the problem of nested networks and their consequences. This article is concerned regarding a problem of nested network called pinball route problem and proposes a solution to eliminate the above problem. The proposed mechanism is implemented using NS2 simulation tool and it is found that the proposed mechanism efficiently reduces the overload caused by the pinball route problem.Keywords: Mobile IP, Pinball routing problem, NEMO
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18314623 A Video Watermarking Algorithm Based on Chaotic and Wavelet Neural Network
Authors: Jiadong Liang
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This paper presented a video watermarking algorithm based on wavelet chaotic neural network. First, to enhance binary image’s security, the algorithm encrypted it with double chaotic based on Arnold and Logistic map, Then, the host video was divided into some equal frames and distilled the key frame through chaotic sequence which generated by Logistic. Meanwhile, we distilled the low frequency coefficients of luminance component and self-adaptively embedded the processed image watermark into the low frequency coefficients of the wavelet transformed luminance component with the wavelet neural network. The experimental result suggested that the presented algorithm has better invisibility and robustness against noise, Gaussian filter, rotation, frame loss and other attacks.
Keywords: Video watermark, double chaotic encryption, wavelet neural network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10434622 Parametric Modeling Approach for Call Holding Times for IP based Public Safety Networks via EM Algorithm
Authors: Badarch Tuyatsetseg
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This paper presents parametric probability density models for call holding times (CHTs) into emergency call center based on the actual data collected for over a week in the public Emergency Information Network (EIN) in Mongolia. When the set of chosen candidates of Gamma distribution family is fitted to the call holding time data, it is observed that the whole area in the CHT empirical histogram is underestimated due to spikes of higher probability and long tails of lower probability in the histogram. Therefore, we provide the Gaussian parametric model of a mixture of lognormal distributions with explicit analytical expressions for the modeling of CHTs of PSNs. Finally, we show that the CHTs for PSNs are fitted reasonably by a mixture of lognormal distributions via the simulation of expectation maximization algorithm. This result is significant as it expresses a useful mathematical tool in an explicit manner of a mixture of lognormal distributions.Keywords: A mixture of lognormal distributions, modeling call holding times, public safety network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16374621 Massive Lesions Classification using Features based on Morphological Lesion Differences
Authors: U. Bottigli, D.Cascio, F. Fauci, B. Golosio, R. Magro, G.L. Masala, P. Oliva, G. Raso, S.Stumbo
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Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based on morphological lesion differences. Some classifiers as a Feed Forward Neural Network, a K-Nearest Neighbours and a Support Vector Machine are used to distinguish the pathological records from the healthy ones. The results obtained in terms of sensitivity (percentage of pathological ROIs correctly classified) and specificity (percentage of non-pathological ROIs correctly classified) will be presented through the Receive Operating Characteristic curve (ROC). In particular the best performances are 88% ± 1 of area under ROC curve obtained with the Feed Forward Neural Network.Keywords: Neural Networks, K-Nearest Neighbours, SupportVector Machine, Computer Aided Diagnosis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13694620 Structural Health Monitoring of Offshore Structures Using Wireless Sensor Networking under Operational and Environmental Variability
Authors: Srinivasan Chandrasekaran, Thailammai Chithambaram, Shihas A. Khader
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The early-stage damage detection in offshore structures requires continuous structural health monitoring and for the large area the position of sensors will also plays an important role in the efficient damage detection. Determining the dynamic behavior of offshore structures requires dense deployment of sensors. The wired Structural Health Monitoring (SHM) systems are highly expensive and always needs larger installation space to deploy. Wireless sensor networks can enhance the SHM system by deployment of scalable sensor network, which consumes lesser space. This paper presents the results of wireless sensor network based Structural Health Monitoring method applied to a scaled experimental model of offshore structure that underwent wave loading. This method determines the serviceability of the offshore structure which is subjected to various environment loads. Wired and wireless sensors were installed in the model and the response of the scaled BLSRP model under wave loading was recorded. The wireless system discussed in this study is the Raspberry pi board with Arm V6 processor which is programmed to transmit the data acquired by the sensor to the server using Wi-Fi adapter, the data is then hosted in the webpage. The data acquired from the wireless and wired SHM systems were compared and the design of the wireless system is verified.Keywords: Condition assessment, damage detection, structural health monitoring, structural response, wireless sensor network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 29544619 Application of the Neural Network to the Synthesis of Vertical Dipole Antenna over Imperfect Ground
Authors: Kais Hafsaoui
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In this paper, we propose to study the synthesis of the vertical dipole antenna over imperfect ground. The synthesis implementation-s method for this type of antenna permits to approach the appropriated radiance-s diagram. The used approach is based on neural network. Our main contribution in this paper is the extension of a synthesis model of this vertical dipole antenna over imperfect ground.Keywords: Vertical dipole antenna, imperfect ground, neural network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11894618 Persian Printed Numeral Characters Recognition Using Geometrical Central Moments and Fuzzy Min-Max Neural Network
Authors: Hamid Reza Boveiri
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In this paper, a new proposed system for Persian printed numeral characters recognition with emphasis on representation and recognition stages is introduced. For the first time, in Persian optical character recognition, geometrical central moments as character image descriptor and fuzzy min-max neural network for Persian numeral character recognition has been used. Set of different experiments on binary images of regular, translated, rotated and scaled Persian numeral characters has been done and variety of results has been presented. The best result was 99.16% correct recognition demonstrating geometrical central moments and fuzzy min-max neural network are adequate for Persian printed numeral character recognition.Keywords: Fuzzy min-max neural network, geometrical centralmoments, optical character recognition, Persian digits recognition, Persian printed numeral characters recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17104617 Face Recognition using Radial Basis Function Network based on LDA
Authors: Byung-Joo Oh
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This paper describes a method to improve the robustness of a face recognition system based on the combination of two compensating classifiers. The face images are preprocessed by the appearance-based statistical approaches such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). LDA features of the face image are taken as the input of the Radial Basis Function Network (RBFN). The proposed approach has been tested on the ORL database. The experimental results show that the LDA+RBFN algorithm has achieved a recognition rate of 93.5%
Keywords: Face recognition, linear discriminant analysis, radial basis function network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21044616 Internet Bandwidth Network Quality Management: The Case Study of Telecom Organization of Thailand
Authors: Sriaroonnirun Sittha, Rotchanakitumnuai Siriluck
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This paper addresses a current problem that occurs among Thai internet service providers with regard to bandwidth network quality management. The IPSTAR department of Telecom Organization of Thailand public company (TOT); the largest internet service provider in Thailand, is the case study to analyze the problem that exists. The Internet bandwidth network quality management (iBWQM) framework is mainly applied to the problem that has been found. Bandwidth management policy (BMP) and quality of service (QoS) are two antecedents of iBWQM. This paper investigates internet user behavior, marketing demand and network operation views in order to determine bandwidth management policy (e.g. quota management, scheduling and malicious management). The congestion of bandwidth is also analyzed to enhance quality of service (QoS). Moreover, the iBWQM framework is able to improve the quality of service and increase bandwidth utilization, minimize complaint rate concerns to slow speed, and provide network planning guidelines through Thai Internet services providers.
Keywords: Internet bandwidth management, Internet serviceprovider, Internet usage behavior, Quality of Service.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 26344615 Real-Time Recognition of Dynamic Hand Postures on a Neuromorphic System
Authors: Qian Liu, Steve Furber
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To explore how the brain may recognise objects in its general,accurate and energy-efficient manner, this paper proposes the use of a neuromorphic hardware system formed from a Dynamic Video Sensor (DVS) silicon retina in concert with the SpiNNaker real-time Spiking Neural Network (SNN) simulator. As a first step in the exploration on this platform a recognition system for dynamic hand postures is developed, enabling the study of the methods used in the visual pathways of the brain. Inspired by the behaviours of the primary visual cortex, Convolutional Neural Networks (CNNs) are modelled using both linear perceptrons and spiking Leaky Integrate-and-Fire (LIF) neurons. In this study’s largest configuration using these approaches, a network of 74,210 neurons and 15,216,512 synapses is created and operated in real-time using 290 SpiNNaker processor cores in parallel and with 93.0% accuracy. A smaller network using only 1/10th of the resources is also created, again operating in real-time, and it is able to recognise the postures with an accuracy of around 86.4% - only 6.6% lower than the much larger system. The recognition rate of the smaller network developed on this neuromorphic system is sufficient for a successful hand posture recognition system, and demonstrates a much improved cost to performance trade-off in its approach.
Keywords: Spiking neural network (SNN), convolutional neural network (CNN), posture recognition, neuromorphic system.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20374614 MITAutomatic ECG Beat Tachycardia Detection Using Artificial Neural Network
Authors: R. Amandi, A. Shahbazi, A. Mohebi, M. Bazargan, Y. Jaberi, P. Emadi, A. Valizade
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The application of Neural Network for disease diagnosis has made great progress and is widely used by physicians. An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which was the great motivation towards our study. In our work, tachycardia features obtained are used for the training and testing of a Neural Network. In this study we are using Fuzzy Probabilistic Neural Networks as an automatic technique for ECG signal analysis. As every real signal recorded by the equipment can have different artifacts, we needed to do some preprocessing steps before feeding it to our system. Wavelet transform is used for extracting the morphological parameters of the ECG signal. The outcome of the approach for the variety of arrhythmias shows the represented approach is superior than prior presented algorithms with an average accuracy of about %95 for more than 7 tachy arrhythmias.Keywords: Fuzzy Logic, Probabilistic Neural Network, Tachycardia, Wavelet Transform.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22714613 A Taxonomy of Routing Protocols in Wireless Sensor Networks
Authors: A. Kardi, R. Zagrouba, M. Alqahtani
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The Internet of Everything (IoE) presents today a very attractive and motivating field of research. It is basically based on Wireless Sensor Networks (WSNs) in which the routing task is the major analysis topic. In fact, it directly affects the effectiveness and the lifetime of the network. This paper, developed from recent works and based on extensive researches, proposes a taxonomy of routing protocols in WSNs. Our main contribution is that we propose a classification model based on nine classes namely application type, delivery mode, initiator of communication, network architecture, path establishment (route discovery), network topology (structure), protocol operation, next hop selection and latency-awareness and energy-efficient routing protocols. In order to provide a total classification pattern to serve as reference for network designers, each class is subdivided into possible subclasses, presented, and discussed using different parameters such as purposes and characteristics.
Keywords: WSNs, sensor, routing protocols, survey.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10184612 A Medical Resource Forecasting Model for Emergency Room Patients with Acute Hepatitis
Authors: R. J. Kuo, W. C. Cheng, W. C. Lien, T. J. Yang
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Taiwan is a hyper endemic area for the Hepatitis B virus (HBV). The estimated total number of HBsAg carriers in the general population who are more than 20 years old is more than 3 million. Therefore, a case record review is conducted from January 2003 to June 2007 for all patients with a diagnosis of acute hepatitis who were admitted to the Emergency Department (ED) of a well-known teaching hospital. The cost for the use of medical resources is defined as the total medical fee. In this study, principal component analysis (PCA) is firstly employed to reduce the number of dimensions. Support vector regression (SVR) and artificial neural network (ANN) are then used to develop the forecasting model. A total of 117 patients meet the inclusion criteria. 61% patients involved in this study are hepatitis B related. The computational result shows that the proposed PCA-SVR model has superior performance than other compared algorithms. In conclusion, the Child-Pugh score and echogram can both be used to predict the cost of medical resources for patients with acute hepatitis in the ED.
Keywords: Acute hepatitis, Medical resource cost, Artificial neural network, Support vector regression.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19094611 Artificial Neural Network Models of the Ruminal pH in Holstein Steers
Authors: Alireza Vakili, Mohsen Danesh Mesgaran, Majid Abdollazade
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In this study four Holstein steers with rumen fistula fed 7 kg of dry matter (DM) of diets differing in concentrate to alfalfa hay ratios as 60:40, 70:30, 80:20, and 90:10 in a 4 × 4 latin square design. The pH of the ruminal fluid was measured before the morning feeding (0.0 h) to 8 h post feeding. In this study, a two-layered feed-forward neural network trained by the Levenberg-Marquardt algorithm was used for modelling of ruminal pH. The input variables of the network were time, concentrate to alfalfa hay ratios (C/F), non fiber carbohydrate (NFC) and neutral detergent fiber (NDF). The output variable was the ruminal pH. The modeling results showed that there was excellent agreement between the experimental data and predicted values, with a high determination coefficient (R2 >0.96). Therefore, we suggest using these model-derived biological values to summarize continuously recorded pH data.Keywords: Ruminal pH, Artificial Neural Network (ANN), Non Fiber Carbohydrate, Neutral Detergent Fiber.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15124610 Pattern Classification of Back-Propagation Algorithm Using Exclusive Connecting Network
Authors: Insung Jung, Gi-Nam Wang
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The objective of this paper is to a design of pattern classification model based on the back-propagation (BP) algorithm for decision support system. Standard BP model has done full connection of each node in the layers from input to output layers. Therefore, it takes a lot of computing time and iteration computing for good performance and less accepted error rate when we are doing some pattern generation or training the network. However, this model is using exclusive connection in between hidden layer nodes and output nodes. The advantage of this model is less number of iteration and better performance compare with standard back-propagation model. We simulated some cases of classification data and different setting of network factors (e.g. hidden layer number and nodes, number of classification and iteration). During our simulation, we found that most of simulations cases were satisfied by BP based using exclusive connection network model compared to standard BP. We expect that this algorithm can be available to identification of user face, analysis of data, mapping data in between environment data and information.Keywords: Neural network, Back-propagation, classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16444609 On Simulation based WSN Multi-Parametric Performance Analysis
Authors: Ch. Antonopoulos, Th. Kapourniotis, V. Triantafillou
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Optimum communication and performance in Wireless Sensor Networks, constitute multi-facet challenges due to the specific networking characteristics as well as the scarce resource availability. Furthermore, it is becoming increasingly apparent that isolated layer based approaches often do not meet the demands posed by WSNs applications due to omission of critical inter-layer interactions and dependencies. As a counterpart, cross-layer is receiving high interest aiming to exploit these interactions and increase network performance. However, in order to clearly identify existing dependencies, comprehensive performance studies are required evaluating the effect of different critical network parameters on system level performance and behavior.This paper-s main objective is to address the need for multi-parametric performance evaluations considering critical network parameters using a well known network simulator, offering useful and practical conclusions and guidelines. The results reveal strong dependencies among considered parameters which can be utilized by and drive future research efforts, towards designing and implementing highly efficient protocols and architectures.Keywords: Wireless sensor network, Communication Systems, cross-layer architectures, simulation based performance evaluation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15134608 Control Chart Pattern Recognition Using Wavelet Based Neural Networks
Authors: Jun Seok Kim, Cheong-Sool Park, Jun-Geol Baek, Sung-Shick Kim
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Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.
Keywords: Control chart pattern recognition, Multi-resolution wavelet analysis, Bi-directional Kohonen network, Back-propagation network, Feature extraction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2465