Search results for: Wireless networks
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2217

Search results for: Wireless networks

1767 Novel Approach for Promoting the Generalization Ability of Neural Networks

Authors: Naiqin Feng, Fang Wang, Yuhui Qiu

Abstract:

A new approach to promote the generalization ability of neural networks is presented. It is based on the point of view of fuzzy theory. This approach is implemented through shrinking or magnifying the input vector, thereby reducing the difference between training set and testing set. It is called “shrinking-magnifying approach" (SMA). At the same time, a new algorithm; α-algorithm is presented to find out the appropriate shrinking-magnifying-factor (SMF) α and obtain better generalization ability of neural networks. Quite a few simulation experiments serve to study the effect of SMA and α-algorithm. The experiment results are discussed in detail, and the function principle of SMA is analyzed in theory. The results of experiments and analyses show that the new approach is not only simpler and easier, but also is very effective to many neural networks and many classification problems. In our experiments, the proportions promoting the generalization ability of neural networks have even reached 90%.

Keywords: Fuzzy theory, generalization, misclassification rate, neural network.

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1766 Investigation of Some Technical Indexes inStock Forecasting Using Neural Networks

Authors: Myungsook Klassen

Abstract:

Training neural networks to capture an intrinsic property of a large volume of high dimensional data is a difficult task, as the training process is computationally expensive. Input attributes should be carefully selected to keep the dimensionality of input vectors relatively small. Technical indexes commonly used for stock market prediction using neural networks are investigated to determine its effectiveness as inputs. The feed forward neural network of Levenberg-Marquardt algorithm is applied to perform one step ahead forecasting of NASDAQ and Dow stock prices.

Keywords: Stock Market Prediction, Neural Networks, Levenberg-Marquadt Algorithm, Technical Indexes

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1765 Implementation of IEEE 802.15.4 Packet Analyzer

Authors: Sung Jun Ban, Hyeonwoo Cho, ChangWoo Lee, Sang Woo Kim

Abstract:

A packet analyzer is a tool for debugging sensor network systems and is convenient for developers. In this paper, we introduce a new packet analyzer based on an embedded system. The proposed packet analyzer is compatible with IEEE 802.15.4, which is suitable for the wireless communication standard for sensor networks, and is available for remote control by adopting a server-client scheme based on the Ethernet interface. To confirm the operations of the packet analyzer, we have developed two types of sensor nodes based on PIC4620 and ATmega128L microprocessors and tested the functions of the proposed packet analyzer by obtaining the packets from the sensor nodes.

Keywords: Sensor network, embedded system, packet analyzer.

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1764 Dynamic Admission Control for Quality of Service in IP Networks

Authors: J. Kasigwa, V. Baryamureeba, D. Williams

Abstract:

The goal of admission control is to support the Quality of Service demands of real-time applications via resource reservation in IP networks. In this paper we introduce a novel Dynamic Admission Control (DAC) mechanism for IP networks. The DAC dynamically allocates network resources using the previous network pattern for each path and uses the dynamic admission algorithm to improve bandwidth utilization using bandwidth brokers. We evaluate the performance of the proposed mechanism through trace-driven simulation experiments in view point of blocking probability, throughput and normalized utilization.

Keywords: Bandwidth broker, dynamic admission control(DAC), IP networks, quality of service, real-time flows.

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1763 Modeling and Prediction of Zinc Extraction Efficiency from Concentrate by Operating Condition and Using Artificial Neural Networks

Authors: S. Mousavian, D. Ashouri, F. Mousavian, V. Nikkhah Rashidabad, N. Ghazinia

Abstract:

PH, temperature and time of extraction of each stage,  agitation speed and delay time between stages effect on efficiency of  zinc extraction from concentrate. In this research, efficiency of zinc  extraction was predicted as a function of mentioned variable by  artificial neural networks (ANN). ANN with different layer was  employed and the result show that the networks with 8 neurons in  hidden layer has good agreement with experimental data.

 

Keywords: Zinc extraction, Efficiency, Neural networks, Operating condition.

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1762 Improvement of Ground Truth Data for Eye Location on Infrared Driver Recordings

Authors: Sorin Valcan, Mihail Găianu

Abstract:

Labeling is a very costly and time consuming process which aims to generate datasets for training neural networks in several functionalities and projects. For driver monitoring system projects, the need of labeled images has a significant impact on the budget and distribution of effort. This paper presents the modifications done to a ground truth data generation algorithm for 2D eyes location on infrared images with drivers in order to improve the quality of the data and performance of the trained neural networks. The algorithm restrictions become tougher which makes it more accurate but also less constant. The resulting dataset becomes smaller and shall not be altered by any kind of manual labels adjustment before being used in the neural networks training process. These changes resulted in a much better performance of the trained neural networks.

Keywords: Labeling automation, infrared camera, driver monitoring, eye detection, Convolutional Neural Networks.

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1761 Wireless Sensor Networks for Swiftlet Farms Monitoring

Authors: Al-Khalid Othman, Wan A. Wan Zainal Abidin, Kee M. Lee, Hushairi Zen, Tengku. M. A. Zulcaffle, Kuryati Kipli

Abstract:

This paper provides an in-depth study of Wireless Sensor Network (WSN) application to monitor and control the swiftlet habitat. A set of system design is designed and developed that includes the hardware design of the nodes, Graphical User Interface (GUI) software, sensor network, and interconnectivity for remote data access and management. System architecture is proposed to address the requirements for habitat monitoring. Such applicationdriven design provides and identify important areas of further work in data sampling, communications and networking. For this monitoring system, a sensor node (MTS400), IRIS and Micaz radio transceivers, and a USB interfaced gateway base station of Crossbow (Xbow) Technology WSN are employed. The GUI of this monitoring system is written using a Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) along with Xbow Technology drivers provided by National Instrument. As a result, this monitoring system is capable of collecting data and presents it in both tables and waveform charts for further analysis. This system is also able to send notification message by email provided Internet connectivity is available whenever changes on habitat at remote sites (swiftlet farms) occur. Other functions that have been implemented in this system are the database system for record and management purposes; remote access through the internet using LogMeIn software. Finally, this research draws a conclusion that a WSN for monitoring swiftlet habitat can be effectively used to monitor and manage swiftlet farming industry in Sarawak.

Keywords: Swiftlet, WSN, Habitat Monitoring, Networking.

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1760 Prediction of Vapor Liquid Equilibrium for Dilute Solutions of Components in Ionic Liquid by Neural Networks

Authors: S. Mousavian, A. Abedianpour, A. Khanmohammadi, S. Hematian, Gh. Eidi Veisi

Abstract:

Ionic liquids are finding a wide range of applications from reaction media to separations and materials processing. In these applications, Vapor–Liquid equilibrium (VLE) is the most important one. VLE for six systems at 353 K and activity coefficients at infinite dilution [(γ)_i^∞] for various solutes (alkanes, alkenes, cycloalkanes, cycloalkenes, aromatics, alcohols, ketones, esters, ethers, and water) in the ionic liquids (1-ethyl-3-methylimidazolium bis (trifluoromethylsulfonyl)imide [EMIM][BTI], 1-hexyl-3-methyl imidazolium bis (trifluoromethylsulfonyl) imide [HMIM][BTI], 1-octyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide [OMIM][BTI], and 1-butyl-1-methylpyrrolidinium bis (trifluoromethylsulfonyl) imide [BMPYR][BTI]) have been used to train neural networks in the temperature range from (303 to 333) K. Densities of the ionic liquids, Hildebrant constant of substances, and temperature were selected as input of neural networks. The networks with different hidden layers were examined. Networks with seven neurons in one hidden layer have minimum error and good agreement with experimental data.

Keywords: Ionic liquid, Neural networks, VLE, Dilute solution.

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1759 Investigation of Improved Chaotic Signal Tracking by Echo State Neural Networks and Multilayer Perceptron via Training of Extended Kalman Filter Approach

Authors: Farhad Asadi, S. Hossein Sadati

Abstract:

This paper presents a prediction performance of feedforward Multilayer Perceptron (MLP) and Echo State Networks (ESN) trained with extended Kalman filter. Feedforward neural networks and ESN are powerful neural networks which can track and predict nonlinear signals. However, their tracking performance depends on the specific signals or data sets, having the risk of instability accompanied by large error. In this study we explore this process by applying different network size and leaking rate for prediction of nonlinear or chaotic signals in MLP neural networks. Major problems of ESN training such as the problem of initialization of the network and improvement in the prediction performance are tackled. The influence of coefficient of activation function in the hidden layer and other key parameters are investigated by simulation results. Extended Kalman filter is employed in order to improve the sequential and regulation learning rate of the feedforward neural networks. This training approach has vital features in the training of the network when signals have chaotic or non-stationary sequential pattern. Minimization of the variance in each step of the computation and hence smoothing of tracking were obtained by examining the results, indicating satisfactory tracking characteristics for certain conditions. In addition, simulation results confirmed satisfactory performance of both of the two neural networks with modified parameterization in tracking of the nonlinear signals.

Keywords: Feedforward neural networks, nonlinear signal prediction, echo state neural networks approach, leaking rates, capacity of neural networks.

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1758 Global Exponential Stability of Impulsive BAM Fuzzy Cellular Neural Networks with Time Delays in the Leakage Terms

Authors: Liping Zhang, Kelin Li

Abstract:

In this paper, a class of impulsive BAM fuzzy cellular neural networks with time delays in the leakage terms is formulated and investigated. By establishing a delay differential inequality and M-matrix theory, some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive BAM fuzzy cellular neural networks with time delays in the leakage terms are obtained. In particular, a precise estimate of the exponential convergence rate is also provided, which depends on system parameters and impulsive perturbation intention. It is believed that these results are significant and useful for the design and applications of BAM fuzzy cellular neural networks. An example is given to show the effectiveness of the results obtained here.

Keywords: Global exponential stability, bidirectional associative memory, fuzzy cellular neural networks, leakage delays, impulses.

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1757 0.13-μm CMOS Vector Modulator for Wireless Backhaul System

Authors: J. S. Kim, N. P. Hong

Abstract:

In this paper, a CMOS vector modulator designed for wireless backhaul system based on 802.11ac is presented. A poly phase filter and sign select switches yield two orthogonal signal paths. Two variable gain amplifiers with strongly reduced phase shift of only ±5 ° are used to weight these paths. It has a phase control range of 360 ° and a gain range of -10 dB to 10 dB. The current drawn from a 1.2 V supply amounts 20.4 mA. Using a 0.13 mm technology, the chip die area amounts 1.47x0.75 mm².

Keywords: CMOS, vector modulator, backhaul, 802.11ac.

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1756 Optical Repeater Assisted Visible Light Device-to-Device Communications

Authors: Samrat Vikramaditya Tiwari, Atul Sewaiwar, Yeon-Ho Chung

Abstract:

Device-to-device (D2D) communication is considered a promising technique to provide wireless peer-to-peer communication services. Due to increasing demand on mobile services, available spectrum for radio frequency (RF) based communications becomes scarce. Recently, visible light communications (VLC) has evolved as a high speed wireless data transmission technology for indoor environments with abundant available bandwidth. In this paper, a novel VLC based D2D communication that provides wireless peer-to-peer communication is proposed. Potential low operating power devices for an efficient D2D communication over increasing distance of separation between devices is analyzed. Optical repeaters (OR) are also proposed to enhance the performance in an environment where direct D2D communications yield degraded performance. Simulation results show that VLC plays an important role in providing efficient D2D communication up to a distance of 1 m between devices. It is also found that the OR significantly improves the coverage distance up to 3.5 m.

Keywords: Visible light communication, light emitting diode, device-to-device, optical repeater.

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1755 APPLE: Providing Absolute and Proportional Throughput Guarantees in Wireless LANs

Authors: Zhijie Ma, Qinglin Zhao, Hongning Dai, Huan Zhang

Abstract:

This paper proposes an APPLE scheme that aims at providing absolute and proportional throughput guarantees, and maximizing system throughput simultaneously for wireless LANs with homogeneous and heterogenous traffic. We formulate our objectives as an optimization problem, present its exact and approximate solutions, and prove the existence and uniqueness of the approximate solution. Simulations validate that APPLE scheme is accurate, and the approximate solution can well achieve the desired objectives already.

Keywords: IEEE 802.11e, throughput guarantee, priority.

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1754 An Impairment Sensitive and Reliable SR-ARQ Mechanism for Unreliable Feedback in GPRS

Authors: Mansab Ali, Muhammad Khalid Khan

Abstract:

The advances in wireless communication have opened unlimited horizons but there are some challenges as well. The Nature derived air medium between MS (Mobile Station) and BS (Base Station) is beyond human control and produces channel impairment. The impact of the natural conditions at the air medium is the biggest issue in wireless communication. Natural conditions make reliability more cumbersome; here reliability refers to the efficient recovery of the lost or erroneous data. The SR-ARQ (Selective Repeat-Automatic Repeat Request) protocol is a de facto standard for any wireless technology at the air interface with its standard reliability features. Our focus in this research is on the reliability of the control or feedback signal of the SR-ARQ protocol. The proposed mechanism, RSR-ARQ (Reliable SR-ARQ) is an enhancement of the SR-ARQ protocol that has ensured the reliability of the control signals through channel impairment sensitive mechanism. We have modeled the system under two-state discrete time Markov Channel. The simulation results demonstrate the better recovery of the lost or erroneous data that will increase the overall system performance.

Keywords: ISR-ARQ, MAA, RSR-ARQ, SAA.

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1753 A New Recognition Scheme for Machine- Printed Arabic Texts based on Neural Networks

Authors: Z. Shaaban

Abstract:

This paper presents a new approach to tackle the problem of recognizing machine-printed Arabic texts. Because of the difficulty of recognizing cursive Arabic words, the text has to be normalized and segmented to be ready for the recognition stage. The new scheme for recognizing Arabic characters depends on multiple parallel neural networks classifier. The classifier has two phases. The first phase categories the input character into one of eight groups. The second phase classifies the character into one of the Arabic character classes in the group. The system achieved high recognition rate.

Keywords: Neural Networks, character recognition, feature extraction, multiple networks, Arabic text.

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1752 RTCoord: A Methodology to Design WSAN Applications

Authors: J. Barbarán, M. Díaz, I. Esteve, D. Garrido, L. Llopis, B. Rubio

Abstract:

Wireless Sensor and Actor Networks (WSANs) constitute an emerging and pervasive technology that is attracting increasing interest in the research community for a wide range of applications. WSANs have two important requirements: coordination interactions and real-time communication to perform correct and timely actions. This paper introduces a methodology to facilitate the task of the application programmer focusing on the coordination and real-time requirements of WSANs. The methodology proposed in this model uses a real-time component model, UM-RTCOM, which will help us to achieve the design and implementation of applications in WSAN by using the component oriented paradigm. This will help us to develop software components which offer some very interesting features, such as reusability and adaptability which are very suitable for WSANs as they are very dynamic environments with rapidly changing conditions. In addition, a high-level coordination model based on tuple channels (TC-WSAN) is integrated into the methodology by providing a component-based specification of this model in UM-RTCOM; this will allow us to satisfy both sensor-actor and actor-actor coordination requirements in WSANs. Finally, we present in this paper the design and implementation of an application which will help us to show how the methodology can be easily used in order to achieve the development of WSANs applications.

Keywords: Sensor networks, real time and embedded systems.

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1751 Almost Periodic Solution for an Impulsive Neural Networks with Distributed Delays

Authors: Lili Wang

Abstract:

By using the estimation of the Cauchy matrix of linear impulsive differential equations and Banach fixed point theorem as well as Gronwall-Bellman’s inequality, some sufficient conditions are obtained for the existence and exponential stability of almost periodic solution for an impulsive neural networks with distributed delays. An example is presented to illustrate the feasibility and  effectiveness of the results.

Keywords: Almost periodic solution, Exponential stability, Neural networks, Impulses.

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1750 Performance Evaluation of Purely Mechanical Wireless In-Mould Sensor for Injection Moulding

Authors: Florian Müller, Christian Kukla, Thomas Lucyshyn, Clemens Holzer

Abstract:

In this paper, the influencing parameters of a novel purely mechanical wireless in-mould injection moulding sensor were investigated. The sensor is capable of detecting the melt front at predefined locations inside the mould. The sensor comprises a movable pin which acts as the sensor element generating structure-borne sound triggered by the passing melt front. Due to the sensor design, melt pressure is the driving force. For pressure level measurement during pin movement a pressure transducer located at the same position as the movable pin. By deriving a mathematical model for the mechanical movement, dominant process parameters could be investigated towards their impact on the melt front detection characteristic. It was found that the sensor is not affected by the investigated parameters enabling it for reliable melt front detection. In addition, it could be proved that the novel sensor is in comparable range to conventional melt front detection sensors.

Keywords: Injection Moulding, In-Mould Sensor, Structure-Borne Sound, Wireless Sensor

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1749 Data Rate Based Grouping Scheme for Cooperative Communications in Wireless LANs

Authors: Sunmyeng Kim

Abstract:

IEEE 802.11a/b/g standards provide multiple transmission rates, which can be changed dynamically according to the channel condition. Cooperative communications were introduced to improve the overall performance of wireless LANs with the help of relay nodes with higher transmission rates. The cooperative communications are based on the fact that the transmission is much faster when sending data packets to a destination node through a relay node with higher transmission rate, rather than sending data directly to the destination node at low transmission rate. To apply the cooperative communications in wireless LAN, several MAC protocols have been proposed. Some of them can result in collisions among relay nodes in a dense network. In order to solve this problem, we propose a new protocol. Relay nodes are grouped based on their transmission rates. And then, relay nodes only in the highest group try to get channel access. Performance evaluation is conducted using simulation, and shows that the proposed protocol significantly outperforms the previous protocol in terms of throughput and collision probability.

Keywords: Cooperative communications, MAC protocol, relay node, WLAN.

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1748 0.13-µm Complementary Metal-Oxide Semiconductor Vector Modulator for Beamforming System

Authors: J. S. Kim

Abstract:

This paper presents a 0.13-µm Complementary Metal-Oxide Semiconductor (CMOS) vector modulator for beamforming system. The vector modulator features a 360° phase and gain range of -10 dB to 10 dB with a root mean square phase and amplitude error of only 2.2° and 0.45 dB, respectively. These features make it a suitable for wireless backhaul system in the 5 GHz industrial, scientific, and medical (ISM) bands. It draws a current of 20.4 mA from a 1.2 V supply. The total chip size is 1.87x1.34 mm².

Keywords: CMOS, vector modulator, beamforming, wireless backhaul, ISM.

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1747 Delay-Distribution-Dependent Stability Criteria for BAM Neural Networks with Time-Varying Delays

Authors: J.H. Park, S. Lakshmanan, H.Y. Jung, S.M. Lee

Abstract:

This paper is concerned with the delay-distributiondependent stability criteria for bidirectional associative memory (BAM) neural networks with time-varying delays. Based on the Lyapunov-Krasovskii functional and stochastic analysis approach, a delay-probability-distribution-dependent sufficient condition is derived to achieve the globally asymptotically mean square stable of the considered BAM neural networks. The criteria are formulated in terms of a set of linear matrix inequalities (LMIs), which can be checked efficiently by use of some standard numerical packages. Finally, a numerical example and its simulation is given to demonstrate the usefulness and effectiveness of the proposed results.

Keywords: BAM neural networks, Probabilistic time-varying delays, Stability criteria.

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1746 A New Robust Stability Criterion for Dynamical Neural Networks with Mixed Time Delays

Authors: Guang Zhou, Shouming Zhong

Abstract:

In this paper, we investigate the problem of the existence, uniqueness and global asymptotic stability of the equilibrium point for a class of neural networks, the neutral system has mixed time delays and parameter uncertainties. Under the assumption that the activation functions are globally Lipschitz continuous, we drive a new criterion for the robust stability of a class of neural networks with time delays by utilizing the Lyapunov stability theorems and the Homomorphic mapping theorem. Numerical examples are given to illustrate the effectiveness and the advantage of the proposed main results.

Keywords: Neural networks, Delayed systems, Lyapunov function, Stability analysis.

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1745 Safety of Industrial Networks

Authors: P. Vazan, P. Tanuska, M. Kebisek, S. Duchovicova

Abstract:

The paper deals with communication standards for control and production system. The authors formulate the requirements for communication security protection. The paper is focused on application protocols of the industrial networks and their basic classification. The typical attacks are analysed and the safety protection, based on requirements for specific industrial network is suggested and defined in this paper.

Keywords: Application protocols, communication standards, industrial networks.

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1744 Fast Forecasting of Stock Market Prices by using New High Speed Time Delay Neural Networks

Authors: Hazem M. El-Bakry, Nikos Mastorakis

Abstract:

Fast forecasting of stock market prices is very important for strategic planning. In this paper, a new approach for fast forecasting of stock market prices is presented. Such algorithm uses new high speed time delay neural networks (HSTDNNs). The operation of these networks relies on performing cross correlation in the frequency domain between the input data and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the presented HSTDNNs is less than that needed by traditional time delay neural networks (TTDNNs). Simulation results using MATLAB confirm the theoretical computations.

Keywords: Fast Forecasting, Stock Market Prices, Time Delay NeuralNetworks, Cross Correlation, Frequency Domain.

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1743 A Car Parking Monitoring System Using a Line-Topology Wireless Sensor Network

Authors: Dae Il Kim, Jungho Moon, Tae Yun Chung

Abstract:

This paper presents a car parking monitoring system using a wireless sensor network. The presented sensor network has a line-shaped topology and adopts a TDMA-based protocol for allowing multi-hop communications. Sensor nodes are deployed in the ground of an outdoor parking lot in such a way that a sensor node monitors a parking space. Each sensor node detects the availability of the associated parking space and transmits the detection result to a sink node via intermediate sensor nodes existing between the source sensor node and the sink node. We evaluate the feasibility of the presented sensor network and the TDMA-based communication protocol through experiments using 11 sensor nodes deployed in a real parking lot. The result shows that the presented car parking monitoring system is robust to changes in the communication environments and efficient for monitoring parking spaces of outdoor parking lots.

Keywords: Multi-hop communication, parking monitoring system, TDMA, wireless sensor network.

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1742 A Novel Approach to Positive Almost Periodic Solution of BAM Neural Networks with Time-Varying Delays

Authors: Lili Wang, Meng Hu

Abstract:

In this paper, based on almost periodic functional hull theory and M-matrix theory, some sufficient conditions are established for the existence and uniqueness of positive almost periodic solution for a class of BAM neural networks with time-varying delays. An example is given to illustrate the main results.

Keywords: Delayed BAM neural networks, Hull theorem, Mmatrix, Almost periodic solution, Global exponential stability.

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1741 Wireless Building Monitoring and Control System

Authors: J.-P. Skön, M. Johansson, O. Kauhanen, M. Raatikainen, K. Leiviskä, M. Kolehmainen

Abstract:

The building sector is the largest energy consumer and CO2 emitter in the European Union (EU) and therefore the active reduction of energy consumption and elimination of energy wastage are among the main goals in it. Healthy housing and energy efficiency are affected by many factors which set challenges to monitoring, control and research of indoor air quality (IAQ) and energy consumption, especially in old buildings. These challenges include measurement and equipment costs, for example. Additionally, the measurement results are difficult to interpret and their usage in the ventilation control is also limited when taking into account the energy efficiency of housing at the same time. The main goal of this study is to develop a cost-effective building monitoring and control system especially for old buildings. The starting point or keyword of the development process is a wireless system; otherwise the installation costs become too high. As the main result, this paper describes an idea of a wireless building monitoring and control system. The first prototype of the system has been installed in 10 residential buildings and in 10 school buildings located in the City of Kuopio, Finland.

Keywords: Energy efficiency, Indoor air quality, Monitoring system, Building automation

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1740 Passivity Analysis of Stochastic Neural Networks With Multiple Time Delays

Authors: Biao Qin, Jin Huang, Jiaojiao Ren, Wei Kang

Abstract:

This paper deals with the problem of passivity analysis for stochastic neural networks with leakage, discrete and distributed delays. By using delay partitioning technique, free weighting matrix method and stochastic analysis technique, several sufficient conditions for the passivity of the addressed neural networks are established in terms of linear matrix inequalities (LMIs), in which both the time-delay and its time derivative can be fully considered. A numerical example is given to show the usefulness and effectiveness of the obtained results.

Keywords: Passivity, Stochastic neural networks, Multiple time delays, Linear matrix inequalities (LMIs).

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1739 Low Overhead Dynamic Channel Selection with Cluster-Based Spatial-Temporal Station Reporting in Wireless Networks

Authors: Zeyad Abdelmageid, Xianbin Wang

Abstract:

Choosing the operational channel for a WLAN access point (AP) in WLAN networks has been a static channel assignment process initiated by the user during the deployment process of the AP, which fails to cope with the dynamic conditions of the assigned channel at the station side afterwards. However, the dramatically growing number of Wi-Fi APs and stations operating in the unlicensed band has led to dynamic, distributed and often severe interference. This highlights the urgent need for the AP to dynamically select the best overall channel of operation for the basic service set (BSS) by considering the distributed and changing channel conditions at all stations. Consequently, dynamic channel selection algorithms which consider feedback from the station side have been developed. Despite the significant performance improvement, existing channel selection algorithms suffer from very high feedback overhead. Feedback latency from the STAs, due the high overhead, can cause the eventually selected channel to no longer be optimal for operation due to the dynamic sharing nature of the unlicensed band. This has inspired us to develop our own dynamic channel selection algorithm with reduced overhead through the proposed low-overhead, cluster-based station reporting mechanism. The main idea behind the cluster-based station reporting is the observation that STAs which are very close to each other tend to have very similar channel conditions. Instead of requesting each STA to report on every candidate channel while causing high overhead, the AP divides STAs into clusters then assigns each STA in each cluster one channel to report feedback on. With proper design of the cluster based reporting, the AP does not lose any information about the channel conditions at the station side while reducing feedback overhead. The simulation results show equal performance and at times better performance with a fraction of the overhead. We believe that this algorithm has great potential in designing future dynamic channel selection algorithms with low overhead.

Keywords: Channel assignment, Wi-Fi networks, clustering, DBSCAN, overhead.

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1738 Neural Networks Approaches for Computing the Forward Kinematics of a Redundant Parallel Manipulator

Authors: H. Sadjadian , H.D. Taghirad Member, A. Fatehi

Abstract:

In this paper, different approaches to solve the forward kinematics of a three DOF actuator redundant hydraulic parallel manipulator are presented. On the contrary to series manipulators, the forward kinematic map of parallel manipulators involves highly coupled nonlinear equations, which are almost impossible to solve analytically. The proposed methods are using neural networks identification with different structures to solve the problem. The accuracy of the results of each method is analyzed in detail and the advantages and the disadvantages of them in computing the forward kinematic map of the given mechanism is discussed in detail. It is concluded that ANFIS presents the best performance compared to MLP, RBF and PNN networks in this particular application.

Keywords: Forward Kinematics, Neural Networks, Numerical Solution, Parallel Manipulators.

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