Search results for: capacity of neural networks.

Authors: Mahdi Sadeghian, Mahmoud Hassanlourad, Alireza Ardakani, Reza Dinarvand

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In this research, the behavior of monopiles, under lateral loads, was investigated with vertical and oblique piles by Finite Element Method. In engineering practice when soil-pile interaction comes to the picture some simplifications are applied to reduce the design time. As a simplified replacement of soil and pile interaction analysis, pile could be replaced by a column. The height of the column would be equal to the free length of the pile plus a portion of the embedded length of it. One of the important factors studied in this study was that columns with an equivalent length (free length plus a part of buried depth) could be used instead of soil and pile modeling. The results of the analysis show that the more internal friction angle of the soil increases, the more the bearing capacity of the soil is achieved. This additional length is 6 to 11 times of the pile diameter in dense soil although in loose sandy soil this range might increase.

Keywords: Lateral bearing capacity, pile group, oblique pile, soil-structure interaction, depth of fixity.

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Authors: Taha Landolsi, A. R. Al-Ali, Tarik Ozkul, Mohammad A. Al-Rousan

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In this paper, we present a cost-effective wireless distributed load shedding system for non-emergency scenarios. In power transformer locations where SCADA system cannot be used, the proposed solution provides a reasonable alternative that combines the use of microcontrollers and existing GSM infrastructure to send early warning SMS messages to users advising them to proactively reduce their power consumption before system capacity is reached and systematic power shutdown takes place. A novel communication protocol and message set have been devised to handle the messaging between the transformer sites, where the microcontrollers are located and where the measurements take place, and the central processing site where the database server is hosted. Moreover, the system sends warning messages to the endusers mobile devices that are used as communication terminals. The system has been implemented and tested via different experimental results.

Keywords: Smart Grid, Load shedding, Demand SideManagement, GSM Wireless Networks, SCADA systems.

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Authors: Nassib Abdallah, Pierre Chauvet, Abd El Salam Hajjar, Bassam Daya

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In this paper, we propose an optimized brain computer interface (BCI) system for unspoken speech recognition, based on the fact that the constructions of unspoken words rely strongly on the Wernicke area, situated in the temporal lobe. Our BCI system has four modules: (i) the EEG Acquisition module based on a non-invasive headset with 14 electrodes; (ii) the Preprocessing module to remove noise and artifacts, using the Common Average Reference method; (iii) the Features Extraction module, using Wavelet Packet Transform (WPT); (iv) the Classification module based on a one-hidden layer artificial neural network. The present study consists of comparing the recognition accuracy of 5 Arabic words, when using all the headset electrodes or only the 4 electrodes situated near the Wernicke area, as well as the selection effect of the subbands produced by the WPT module. After applying the articial neural network on the produced database, we obtain, on the test dataset, an accuracy of 83.4% with all the electrodes and all the subbands of 8 levels of the WPT decomposition. However, by using only the 4 electrodes near Wernicke Area and the 6 middle subbands of the WPT, we obtain a high reduction of the dataset size, equal to approximately 19% of the total dataset, with 67.5% of accuracy rate. This reduction appears particularly important to improve the design of a low cost and simple to use BCI, trained for several words.

Keywords: Brain-computer interface, speech recognition, electroencephalography EEG, Wernicke area, artificial neural network.

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Authors: Min Kyung An

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In this paper, we study the data collection problem in Wireless Sensor Networks (WSNs) adopting the two interference models: The graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR). The main issue of the problem is to compute schedules with the minimum number of timeslots, that is, to compute the minimum latency schedules, such that data from every node can be collected without any collision or interference to a sink node. While existing works studied the problem with unit-sized and unbounded-sized message models, we investigate the problem with the bounded-sized message model, and introduce a constant factor approximation algorithm. To the best known of our knowledge, our result is the first result of the data collection problem with bounded-sized model in both interference models.

Keywords: Data collection, collision-free, interference-free, physical interference model, SINR, approximation, bounded-sized message model, wireless sensor networks, WSN.

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Authors: Viraj Tyagi, Ajai Jain, P. K. Jain, Aarushi Jain

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This paper presents a genetic algorithm based loading methodology for a capacity constrained job-shop with the consideration of alternative process plans for each part to be produced. Performance analysis of the proposed methodology is carried out for two case studies by considering two different manufacturing scenarios. Results obtained indicate that the methodology is quite effective in improving the shop load balance, and hence, it can be included in the frameworks of manufacturing planning systems of job-shop oriented industries.

Keywords: Manufacturing planning, loading, genetic algorithm, Job-Shop

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Authors: Selma Sbit, Mohamed Bechir Dadi, Belgacem Chibani Rhaimi

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This work aims to compare various techniques used in order to mitigate Inter-Cell Interference (ICI) in Long Term Evolution (LTE) and LTE-Advanced systems. For that, we will evaluate the performance of each one. In mobile communication networks, systems are limited by ICI particularly caused by deployment of small cells in conventional cell’s implementation. Therefore, various mitigation techniques, named Inter-Cell Interference Coordination techniques (ICIC), enhanced Inter-Cell Interference Coordination (eICIC) techniques and Coordinated Multi-Point transmission and reception (CoMP) are proposed. This paper presents a comparative study of these strategies. It can be concluded that CoMP techniques can ameliorate SINR and capacity system compared to ICIC and eICIC. In fact, SINR value reaches 15 dB for a distance of 0.5 km between user equipment and servant base station if we use CoMP technology whereas it cannot exceed 12 dB and 9 dB for eICIC and ICIC approaches respectively as reflected in simulations.

Keywords: 4th generation, interference, coordination, ICIC.

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Authors: Jarrod Trevathan, Trina Myers

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Proprietary sensor network systems are typically expensive, rigid and difficult to incorporate technologies from other vendors. When using competing and incompatible technologies, a non-proprietary system is complex to create because it requires significant technical expertise and effort, which can be more expensive than a proprietary product. This paper presents the Sensor Abstraction Layer (SAL) that provides middleware architectures with a consistent and uniform view of heterogeneous sensor networks, regardless of the technologies involved. SAL abstracts and hides the hardware disparities and specificities related to accessing, controlling, probing and piloting heterogeneous sensors. SAL is a single software library containing a stable hardware-independent interface with consistent access and control functions to remotely manage the network. The end-user has near-real-time access to the collected data via the network, which results in a cost-effective, flexible and simplified system suitable for novice users. SAL has been used for successfully implementing several low-cost sensor network systems.

Keywords: Sensor networks, hardware abstraction, middleware integration platform, sensor web enablement.

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Authors: Jaipal Bisht, Aditya Goel

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Optical Burst Switching (OBS) is a promising technology for the future generation Internet. Control architecture and Contention resolution are the main issues faced by the Optical Burst Switching networks. In this paper we are only taking care of the Contention problem and to overcome this issue we propose Pre-Deflection Routing with Control Packet Signal Scheme for Contention Resolution in Optical Burst Switch Networks. In this paper Pre-deflection routing approach has been proposed in which routing is carried out in two ways, Shortest Path First (SPF) and Least Hop First (LHF) Routing to forward the clusters and canoes respectively. Hereafter Burst Offset Time Control Algorithm has been proposed where a forward control packet (FCP) collects the congestion price and contention price along its paths. Thereafter a reverse-direction control packet (RCP) sent by destination node which delivers the information of FCP to the source node, and source node uses this information to revise its offset time and burst length.

Keywords: Contention Resolution, FCP, OBS, Offset Time, PST, RCP.

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Authors: V. Narasimha Raghavan, T. Peer Meera Labbai, N. Bhalaji, Suvitha Kesavan

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In this paper, a new approach based on the extent of friendship between the nodes is proposed which makes the nodes to co-operate in an ad hoc environment. The extended DSR protocol is tested under different scenarios by varying the number of malicious nodes and node moving speed. It is also tested varying the number of nodes in simulation used. The result indicates the achieved throughput by extended DSR is greater than the standard DSR and indicates the percentage of malicious drops over total drops are less in the case of extended DSR than the standard DSR.

Keywords: Mobile Adhoc Networks, DSR, Grudger protocol, Nodes.

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Authors: Gitanjali Devi, Kandarpa Kumar Sarma, Pranayee Datta, Anjana Kakoti Mahanta

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Cosmic showers, during the transit through space, produce sub - products as a result of interactions with the intergalactic or interstellar medium which after entering earth generate secondary particles called Extensive Air Shower (EAS). Detection and analysis of High Energy Particle Showers involve a plethora of theoretical and experimental works with a host of constraints resulting in inaccuracies in measurements. Therefore, there exist a necessity to develop a readily available system based on soft-computational approaches which can be used for EAS analysis. This is due to the fact that soft computational tools such as Artificial Neural Network (ANN)s can be trained as classifiers to adapt and learn the surrounding variations. But single classifiers fail to reach optimality of decision making in many situations for which Multiple Classifier System (MCS) are preferred to enhance the ability of the system to make decisions adjusting to finer variations. This work describes the formation of an MCS using Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN) and Probabilistic Neural Network (PNN) with data inputs from correlation mapping Self Organizing Map (SOM) blocks and the output optimized by another SOM. The results show that the setup can be adopted for real time practical applications for prediction of primary energy and location of EAS from density values captured using detectors in a circular grid.

Keywords: EAS, Shower, Core, ANN, Location.

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Authors: Parvinder S. Sandhu, Harpreet Kaur, Amanpreet Singh

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Automatic reusability appraisal is helpful in evaluating the quality of developed or developing reusable software components and in identification of reusable components from existing legacy systems; that can save cost of developing the software from scratch. But the issue of how to identify reusable components from existing systems has remained relatively unexplored. In this research work, structural attributes of software components are explored using software metrics and quality of the software is inferred by different Neural Network based approaches, taking the metric values as input. The calculated reusability value enables to identify a good quality code automatically. It is found that the reusability value determined is close to the manual analysis used to be performed by the programmers or repository managers. So, the developed system can be used to enhance the productivity and quality of software development.

Keywords: Neural Network, Software Reusability, Software Metric, Accuracy, MAE, RMSE.

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Authors: Zöhre Kara Kayikci, Günther Palm

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A word recognition architecture based on a network of neural associative memories and hidden Markov models has been developed. The input stream, composed of subword-units like wordinternal triphones consisting of diphones and triphones, is provided to the network of neural associative memories by hidden Markov models. The word recognition network derives words from this input stream. The architecture has the ability to handle ambiguities on subword-unit level and is also able to add new words to the vocabulary during performance. The architecture is implemented to perform the word recognition task in a language processing system for understanding simple command sentences like “bot show apple".

Keywords: Hebbian learning, hidden Markov models, neuralassociative memories, word recognition.

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Authors: Rama Shanker, Anil Kumar Sachan

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Basic ingredients of concrete are cement, fine aggregate, coarse aggregate and water. To produce a concrete of certain specific properties, optimum proportion of these ingredients are mixed. The important factors which govern the mix design are grade of concrete, type of cement and size, shape and grading of aggregates. Concrete mix design method is based on experimentally evolved empirical relationship between the factors in the choice of mix design. Basic draw backs of this method are that it does not produce desired strength, calculations are cumbersome and a number of tables are to be referred for arriving at trial mix proportion moreover, the variation in attainment of desired strength is uncertain below the target strength and may even fail. To solve this problem, a lot of cubes of standard grades were prepared and attained 28 days strength determined for different combination of cement, fine aggregate, coarse aggregate and water. An artificial neural network (ANN) was prepared using these data. The input of ANN were grade of concrete, type of cement, size, shape and grading of aggregates and output were proportions of various ingredients. With the help of these inputs and outputs, ANN was trained using feed forward back proportion model. Finally trained ANN was validated, it was seen that it gave the result with/ error of maximum 4 to 5%. Hence, specific type of concrete can be prepared from given material properties and proportions of these materials can be quickly evaluated using the proposed ANN.

Keywords: Aggregate Proportions, Artificial Neural Network, Concrete Grade, Concrete Mix Design.

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Authors: T. O. Olwal, K. Djouani, B. J. van Wyk, Y. Hamam, P. Siarry, N. Ntlatlapa

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Multi-Radio Multi-Channel (MRMC) systems are key to power control problems in wireless mesh networks (WMNs). In this paper, we present asynchronous multiple-state based power control for MRMC WMNs. First, WMN is represented as a set of disjoint Unified Channel Graphs (UCGs). Second, each network interface card (NIC) or radio assigned to a unique UCG adjusts transmission power using predicted multiple interaction state variables (IV) across UCGs. Depending on the size of queue loads and intra- and inter-channel states, each NIC optimizes transmission power locally and asynchronously. A new power selection MRMC unification protocol (PMMUP) is proposed that coordinates interactions among radios. The efficacy of the proposed method is investigated through simulations.

Keywords: Asynchronous convergence, Multi-Radio Multi-Channel (MRMC), Power Selection Multi-Radio Multi-Channel Unification Protocol (PMMUP) and Wireless Mesh Networks(WMNs)

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Authors: Somenath Mukherjee, Raj Kumar Samanta, Gautam Sanyal

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Different services based on different switching techniques in wireless networks leads to drastic changes in the properties of network traffic. Because of these diversities in services, network traffic is expected to undergo qualitative and quantitative variations. Hence, assumption of traffic characteristics and the prediction of network events become more complex for the wireless networks. In this paper, the traffic characteristics have been studied by collecting traces from the mobile switching centre (MSC). The traces include initiation and termination time, originating node, home station id, foreign station id. Traffic parameters namely, call interarrival and holding times were estimated statistically. The results show that call inter-arrival and distribution time in this wireless network is heavy-tailed and follow gamma distributions. They are asymptotically long-range dependent. It is also found that the call holding times are best fitted with lognormal distribution. Based on these observations, an analytical model for performance estimation is also proposed.

Keywords: Wireless networks, traffic analysis, long-range dependence, heavy-tailed distribution.

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Authors: Gopu Sreenivasulu, Bimlesh Kumar, Achanta Ramakrishna Rao

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To define or predict incipient motion in an alluvial channel, most of the investigators use a standard or modified form of Shields- diagram. Shields- diagram does give a process to determine the incipient motion parameters but an iterative one. To design properly (without iteration), one should have another equation for resistance. Absence of a universal resistance equation also magnifies the difficulties in defining the model. Neural network technique, which is particularly useful in modeling a complex processes, is presented as a tool complimentary to modeling incipient motion. Present work develops a neural network model employing the RBF network to predict the average velocity u and water depth y based on the experimental data on incipient condition. Based on the model, design curves have been presented for the field application.

Keywords: Incipient motion, Prediction error, Radial-Basisfunction, Sediment transport, Shields' diagram.

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Authors: Daniel Heibert, Brian Lithgow, Kerry Hourigan

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This paper attempts to explain response components of Electrovestibulography (EVestG) using a computer simulation of a three-canal model of the vestibular system. EVestG is a potentially new diagnostic method for Meniere's disease. EVestG is a variant of Electrocochleography (ECOG), which has been used as a standard method for diagnosing Meniere's disease - it can be used to measure the SP/AP ratio, where an SP/AP ratio greater than 0.4-0.5 is indicative of Meniere-s Disease. In EVestG, an applied head tilt replaces the acoustic stimulus of ECOG. The EVestG output is also an SP/AP type plot, where SP is the summing potential, and AP is the action potential amplitude. AP is thought of as being proportional to the size of a population of afferents in an excitatory neural firing state. A simulation of the fluid volume displacement in the vestibular labyrinth in response to various types of head tilts (ipsilateral, backwards and horizontal rotation) was performed, and a simple neural model based on these simulations developed. The simple neural model shows that the change in firing rate of the utricle is much larger in magnitude than the change in firing rates of all three semi-circular canals following a head tilt (except in a horizontal rotation). The data suggests that the change in utricular firing rate is a minimum 2-3 orders of magnitude larger than changes in firing rates of the canals during ipsilateral/backward tilts. Based on these results, the neural response recorded by the electrode in our EVestG recordings is expected to be dominated by the utricle in ipsilateral/backward tilts (It is important to note that the effect of the saccule and efferent signals were not taken into account in this model). If the utricle response dominates the EVestG recordings as the modeling results suggest, then EVestG has the potential to diagnose utricular hair cell damage due to a viral infection (which has been cited as one possible cause of Meniere's Disease).

Keywords: Diagnostic, endolymph hydrops, Meniere's disease, modeling.

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Authors: Sofia Stoica

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In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices over the past five years of 10 major tech companies: Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We implemented and tested three models – a linear regressor model, a k-nearest neighbor model (KNN), and a sequential neural network – and two algorithms – Multiplicative Weight Update and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.

Keywords: Finance, machine learning, opening price, stock market.

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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.

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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.

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Authors: Shan-Jen Cheng, Te-Jen Chang, Kuang-Hsiung Tan, Shou-Ling Kuo

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Polymer Electrolyte Membrane Fuel Cell (PEMFC) is such a time-vary nonlinear dynamic system. The traditional linear modeling approach is hard to estimate structure correctly of PEMFC system. From this reason, this paper presents a nonlinear modeling of the PEMFC using Neural Network Auto-regressive model with eXogenous inputs (NNARX) approach. The multilayer perception (MLP) network is applied to evaluate the structure of the NNARX model of PEMFC. The validity and accuracy of NNARX model are tested by one step ahead relating output voltage to input current from measured experimental of PEMFC. The results show that the obtained nonlinear NNARX model can efficiently approximate the dynamic mode of the PEMFC and model output and system measured output consistently.

Keywords: PEMFC, neural network, nonlinear identification, NNARX.

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Authors: Igor Astrov, Mikhail Pikkov, Rein Paluoja

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This paper focuses on a critical component of the situational awareness (SA), the control of autonomous vertical flight for vectored thrust aerial vehicle (VTAV). With the SA strategy, we proposed a flight control procedure to address the dynamics variation and performance requirement difference of flight trajectory for an unmanned helicopter model with vectored thrust configuration. This control strategy for chosen model of VTAV has been verified by simulation of take-off and forward maneuvers using software package Simulink and demonstrated good performance for fast stabilization of motors, consequently, fast SA with economy in energy can be asserted during search-and-rescue operations.

Keywords: Neural network predictive controller, situational awareness, vectored thrust aerial vehicle.

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Authors: Dong Wan-li, Gu Nai-jie, Tu Kun, Bi Kun, Liu Gang

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As originally designed for wired networks, TCP (transmission control protocol) congestion control mechanism is triggered into action when packet loss is detected. This implicit assumption for packet loss mostly due to network congestion does not work well in Mobile Ad Hoc Network, where there is a comparatively high likelihood of packet loss due to channel errors and node mobility etc. Such non-congestion packet loss, when dealt with by congestion control mechanism, causes poor TCP performance in MANET. In this study, we continue to investigate the impact of the interaction between transport protocols and on-demand routing protocols on the performance and stability of 802.11 multihop networks. We evaluate the important wireless networking events caused routing change, and propose a cross layer method to delay the unnecessary routing changes, only need to add a sensitivity parameter α , which represents the on-demand routing-s reaction to link failure of MAC layer. Our proposal is applicable to the plain 802.11 networking environment, the simulation results that this method can remarkably improve the stability and performance of TCP without any modification on TCP and MAC protocol.

Keywords: Mobile ad hoc networks (MANET), on-demandrouting, performance, transmission control protocol (TCP).

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Authors: Chih-Yuan Chang, Jin-Chiuan Chang, San-Shan Hung, Cheng-Jui Hsu

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The heat storage capacity of concrete in building shells is a major reason for excessively large electricity consumption induced by indoor air conditioning. In this research, the previously developed Smart Temperature Information Material (STIM) is embedded in two groups of exterior wall specimens (the control group contains reinforced concrete exterior walls and the experimental group consists of tiled exterior walls). Long term temperature measurements within the concrete are taken by the embedded STIM. Temperature differences between the control group and the experimental group in walls facing the four cardinal directions (east, west, south, and north) are evaluated. This study aims to provide a basic reference for the design of exterior walls and the selection of heat insulation materials.

Keywords: building envelope, sensor, energy, thermal insulation, reinforced concrete

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Authors: Sunmyeng Kim

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IEEE 802.11a/b/g standards support multiple transmission rates. Even though the use of multiple transmission rates increase the WLAN capacity, this feature leads to the performance anomaly problem. Cooperative communication was introduced to relieve the performance anomaly problem. Data packets are delivered to the destination much faster through a relay node with high rate than through direct transmission to the destination at low rate. In the legacy cooperative protocols, a source node chooses a relay node only based on the transmission rate. Therefore, they are not so feasible in multi-flow environments since they do not consider the effect of other flows. To alleviate the effect, we propose a new relay node selection algorithm based on the transmission rate and channel contention level. Performance evaluation is conducted using simulation, and shows that the proposed protocol significantly outperforms the previous protocol in terms of throughput and delay.

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

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Authors: Zhaojun Wang, Zongdi Sun, Zhiyuan Liu

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First of all, the carbon trading price and trading volume in Shanghai are transformed by Fourier transform, and the frequency response diagram is obtained. Then, the frequency response diagram is analyzed and the Blackman filter is designed. The Blackman filter is used to filter, and the carbon trading time domain and frequency response diagram are obtained. After wavelet analysis, the carbon trading data were processed; respectively, we got the average value for each 5 days, 10 days, 20 days, 30 days, and 60 days. Finally, the data are used as input of the Back Propagation Neural Network model for prediction.

Keywords: Shanghai carbon trading, carbon trading price, carbon trading volume, wavelet analysis, BP neural network model.

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Authors: Siliang Wang, Minghui Wang, Jun Hu

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A novel path planning approach is presented to solve optimal path in stochastic, time-varying networks under priori traffic information. Most existing studies make use of dynamic programming to find optimal path. However, those methods are proved to be unable to obtain global optimal value, moreover, how to design efficient algorithms is also another challenge. This paper employs a decision theoretic framework for defining optimal path: for a given source S and destination D in urban transit network, we seek an S - D path of lowest expected travel time where its link travel times are discrete random variables. To solve deficiency caused by the methods of dynamic programming, such as curse of dimensionality and violation of optimal principle, an integer programming model is built to realize assignment of discrete travel time variables to arcs. Simultaneously, pruning techniques are also applied to reduce computation complexity in the algorithm. The final experiments show the feasibility of the novel approach.

Keywords: pruning method, stochastic, time-varying networks, optimal path planning.

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Authors: S. Rao, S. Khavtasi, C. Chassot, N. Van Wambeke, F. Armando, S. P. Romano, T. Castaldi

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The talks about technological convergence had been around for almost twenty years. Today Internet made it possible. And this is not only technical evolution. The way it changed our lives reflected in variety of applications, services and technologies used in day-to-day life. Such benefits imposed even more requirements on heterogeneous and unreliable IP networks. Current paper outlines QoS management system developed in the NetQoS [1] project. It describes an overall architecture of management system for heterogeneous networks and proposes automated multi-layer QoS management. Paper focuses on the structure of the most crucial modules of the system that enable autonomous and multi-layer provisioning and dynamic adaptation.

Keywords: Automated QoS management, multi-layerprovisioning and adaptation, QoS, QoE.

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Authors: Shobha.K.R., K. Rajanikanth

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An on-demand routing protocol for wireless ad hoc networks is one that searches for and attempts to discover a route to some destination node only when a sending node originates a data packet addressed to that node. In order to avoid the need for such a route discovery to be performed before each data packet is sent, such routing protocols must cache routes previously discovered. This paper presents an analysis of the effect of intelligent caching in a non clustered network, using on-demand routing protocols in wireless ad hoc networks. The analysis carried out is based on the Dynamic Source Routing protocol (DSR), which operates entirely on-demand. DSR uses the cache in every node to save the paths that are learnt during route discovery procedure. In this implementation, caching these paths only at intermediate nodes and using the paths from these caches when required is tried. This technique helps in storing more number of routes that are learnt without erasing the entries in the cache, to store a new route that is learnt. The simulation results on DSR have shown that this technique drastically increases the available memory for caching the routes discovered without affecting the performance of the DSR routing protocol in any way, except for a small increase in end to end delay.

Keywords: Caching, DSR, on demand routing, MANET.

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Authors: Sony P. Aquino, Helen F. Gavino, Victorino T. Taylan, Teresito G. Aguinaldo

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A simple mobile engine-driven pneumatic paddy collector made of locally available materials using local manufacturing technology was designed, fabricated, and tested for collecting and bagging of paddy dried on concrete pavement. The pneumatic paddy collector had the following major components: radial flat bladed type centrifugal fan, power transmission system, bagging area, frame and the conveyance system. Results showed significant differences on the collecting capacity, noise level, and fuel consumption when rotational speed of the air mover shaft was varied. Other parameters such as collecting efficiency, air velocity, augmented cracked grain percentage, and germination rate were not significantly affected by varying rotational speed of the air mover shaft. The pneumatic paddy collector had a collecting efficiency of 99.33 % with a collecting capacity of 2685.00 kg/h at maximum rotational speed of centrifugal fan shaft of about 4200 rpm. The machine entailed an investment cost of P 62,829.25. The break-even weight of paddy was 510,606.75 kg/yr at a collecting cost of 0.11 P/kg of paddy. Utilizing the machine for 400 hours per year generated an income of P 23,887.73. The projected time needed to recover cost of the machine based on 2685 kg/h collecting capacity was 2.63 year.

Keywords: Mobile engine-driven pneumatic paddy collector, collecting capacity and efficiency, simple cost analysis.

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