Search results for: echo state networks (ESNs)

Authors: Robert Koch, Mario Golling, Gabi Dreo

Abstract:

During the last couple of years, the degree of dependence on IT systems has reached a dimension nobody imagined to be possible 10 years ago. The increased usage of mobile devices (e.g., smart phones), wireless sensor networks and embedded devices (Internet of Things) are only some examples of the dependency of modern societies on cyber space. At the same time, the complexity of IT applications, e.g., because of the increasing use of cloud computing, is rising continuously. Along with this, the threats to IT security have increased both quantitatively and qualitatively, as recent examples like STUXNET or the supposed cyber attack on Illinois water system are proofing impressively. Once isolated control systems are nowadays often publicly available - a fact that has never been intended by the developers. Threats to IT systems don’t care about areas of responsibility. Especially with regard to Cyber Warfare, IT threats are no longer limited to company or industry boundaries, administrative jurisdictions or state boundaries. One of the important countermeasures is increased cooperation among the participants especially in the field of Cyber Defence. Besides political and legal challenges, there are technical ones as well. A better, at least partially automated exchange of information is essential to (i) enable sophisticated situational awareness and to (ii) counter the attacker in a coordinated way. Therefore, this publication performs an evaluation of state of the art Intrusion Detection Message Exchange protocols in order to guarantee a secure information exchange between different entities.

Keywords: Cyber Defence, Cyber Warfare, Intrusion Detection Information Exchange, Early Warning Systems, Joint Intrusion Detection, Cyber Conflict

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Authors: Yunquan Ke, Chunfang Miao

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In this paper, the exponential stability of periodic solutions in inertial neural networks with unbounded delay are investigated. First, using variable substitution the system is transformed to first order differential equation. Second, by the fixed-point theorem and constructing suitable Lyapunov function, some sufficient conditions guaranteeing the existence and exponential stability of periodic solutions of the system are obtained. Finally, two examples are given to illustrate the effectiveness of the results.

Keywords: Inertial neural networks, unbounded delay, fixed-point theorem, Lyapunov function, periodic solutions, exponential stability.

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Authors: Paul Lajbcygier, Seng Lee

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Co-integration models the long-term, equilibrium relationship of two or more related financial variables. Even if cointegration is found, in the short run, there may be deviations from the long run equilibrium relationship. The aim of this work is to forecast these deviations using neural networks and create a trading strategy based on them. A case study is used: co-integration residuals from Australian Bank Bill futures are forecast and traded using various exogenous input variables combined with neural networks. The choice of the optimal exogenous input variables chosen for each neural network, undertaken in previous work [1], is validated by comparing the forecasts and corresponding profitability of each, using a trading strategy.

Keywords: Artificial neural networks, co-integration, forecasting, trading rule.

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Authors: Tea Poklepović, Zdravka Aljinović, Branka Marasović

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Due to the importance of yield curve and its estimation it is inevitable to have valid methods for yield curve forecasting in cases when there are scarce issues of securities and/or week trade on a secondary market. Therefore in this paper, after the estimation of weekly yield curves on Croatian financial market from October 2011 to August 2012 using Nelson-Siegel and Svensson models, yield curves are forecasted using Vector autoregressive model and Neural networks. In general, it can be concluded that both forecasting methods have good prediction abilities where forecasting of yield curves based on Nelson Siegel estimation model give better results in sense of lower Mean Squared Error than forecasting based on Svensson model Also, in this case Neural networks provide slightly better results. Finally, it can be concluded that most appropriate way of yield curve prediction is Neural networks using Nelson-Siegel estimation of yield curves.

Keywords: Nelson-Siegel model, Neural networks, Svensson model, Vector autoregressive model, Yield curve.

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Authors: Moslem Afrashteh Mehr

Abstract:

Wireless Sensor Networks consist of small battery powered devices with limited energy resources. once deployed, the small sensor nodes are usually inaccessible to the user, and thus replacement of the energy source is not feasible. Hence, One of the most important issues that needs to be enhanced in order to improve the life span of the network is energy efficiency. to overcome this demerit many research have been done. The clustering is the one of the representative approaches. in the clustering, the cluster heads gather data from nodes and sending them to the base station. In this paper, we introduce a dynamic clustering algorithm using genetic algorithm. This algorithm takes different parameters into consideration to increase the network lifetime. To prove efficiency of proposed algorithm, we simulated the proposed algorithm compared with LEACH algorithm using the matlab

Keywords: Wireless Sensor Networks, Clustering, Geneticalgorithm, Energy Consumption

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Authors: Narottam Chand

Abstract:

Wireless sensor networks (WSNs) have gained tremendous attention in recent years due to their numerous applications. Due to the limited energy resource, energy efficient operation of sensor nodes is a key issue in wireless sensor networks. Cooperative caching which ensures sharing of data among various nodes reduces the number of communications over the wireless channels and thus enhances the overall lifetime of a wireless sensor network. In this paper, we propose a cooperative caching scheme called ZCS (Zone Cooperation at Sensors) for wireless sensor networks. In ZCS scheme, one-hop neighbors of a sensor node form a cooperative cache zone and share the cached data with each other. Simulation experiments show that the ZCS caching scheme achieves significant improvements in byte hit ratio and average query latency in comparison with other caching strategies.

Keywords: Admission control, cache replacement, cooperative caching, WSN, zone cooperation

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Authors: L. Parisi

Abstract:

Kinematic data wisely correlate vector quantities in space to scalar parameters in time to assess the degree of symmetry between the intact limb and the amputated limb with respect to a normal model derived from the gait of control group participants. Furthermore, these particular data allow a doctor to preliminarily evaluate the usefulness of a certain rehabilitation therapy. Kinetic curves allow the analysis of ground reaction forces (GRFs) to assess the appropriateness of human motion. Electromyography (EMG) allows the analysis of the fundamental lower limb force contributions to quantify the level of gait asymmetry. However, the use of this technological tool is expensive and requires patient’s hospitalization. This research work suggests overcoming the above limitations by applying artificial neural networks.

Keywords: Kinetics, kinematics, cyclograms, neural networks.

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Authors: A. S. Adelokun, O. O. Gambo, A. A. Adegboye

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This study estimates the private cost of Humanities and Education programs in public universities in Osun state, Nigeria, as well as the private monetary returns to Humanities and Education programs in public universities in the state. It also estimates the private rates of return to Humanities and Education programmes in public universities in Osun state; with the view of providing information on the relative profitability of investments in Humanities and Education programs in public universities in Osun state. The study adopted a descriptive survey research design. The population for the study consisted of all Humanities and Education students from public universities in Osun State and all Humanities and Education graduates who are workers in Osun state establishments. The sample was made up of 600 students and 120 workers. The students were selected through simple random sampling technique from the two public universities in the state while the workers were purposively selected from Osun state establishments. These workers were graduates of Humanities and Education programs. The selected programs included Bachelor of Arts (B.A.) in English, Bachelor of Education (B.Ed.) in English, B.A. in Religious Studies, B.Ed. in Religious Studies, B.A. in Yoruba and B.Ed. in Yoruba. Two research instruments were used, namely: Private Costs of University Education Questionnaire (PCUEQ) and Age Education Earnings of Workers Questionnaire (AEEWQ). The data were analyzed using compounding and discount cash flow techniques. The results showed that the private costs of Humanities and Education programs in public universities in Osun state were N855,935.59 and N694,269.34 respectively. The private monetary returns to Humanities and Education programs in public universities in the State were N9,052,859.28 and N9,052,859.28, respectively. The private rates of return to Humanities and Education programmes in public universities in Osun state were 27.36% and 34.40% respectively. The study concluded that it was more profitable to invest in Education programs than in Humanities programs at public universities in Osun state, Nigeria.

Keywords: Rates of return, private cost, investment, education.

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Authors: Rosa Nurtazina, Yerkebulan Zhumashov, Maral Tomanova

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Humanity is entering an era when "virtual reality" as the image of the world created by the media with the help of the Internet does not match the reality in many respects, when new communication technologies create a fundamentally different and previously unknown "global space". According to these technologies, the state begins to change the basic technology of political communication of the state and society, the state and the state. Nowadays image of the state becomes the most important tool and technology.

Image is a purposefully created image granting political object (person, organization, country, etc.) certain social and political values and promoting more emotional perception.

Political image of the state plays an important role in international relations. The success of the country's foreign policy, development of trade and economic relations with other countries depends on whether it is positive or negative. Foreign policy image has an impact on political processes taking place in the state: the negative image of the country's can be used by opposition forces as one of the arguments to criticize the government and its policies.

Keywords: Image of the country, country's image classification, function of the country image, country's image components.

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Authors: W. D. Wan Rosli, Z. Zainuddin, R. Lanouette, S. Sathasivam

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This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of Pulping of Sugar Maple problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified problem where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.

Keywords: Convergence, Modeling, Neural Networks, Preconditioned Conjugate Gradient.

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Authors: Safaa R. Amer

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Missing data yields many analysis challenges. In case of complex survey design, in addition to dealing with missing data, researchers need to account for the sampling design to achieve useful inferences. Methods for incorporating sampling weights in neural network imputation were investigated to account for complex survey designs. An estimate of variance to account for the imputation uncertainty as well as the sampling design using neural networks will be provided. A simulation study was conducted to compare estimation results based on complete case analysis, multiple imputation using a Markov Chain Monte Carlo, and neural network imputation. Furthermore, a public-use dataset was used as an example to illustrate neural networks imputation under a complex survey design

Keywords: Complex survey, estimate, imputation, neural networks, variance.

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Authors: T.G. Basavaraju, Subir Kumar Sarkar, C Puttamadappa

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Mobile Ad hoc Networks is an autonomous system of mobile nodes connected by multi-hop wireless links without centralized infrastructure support. As mobile communication gains popularity, the need for suitable ad hoc routing protocols will continue to grow. Efficient dynamic routing is an important research challenge in such a network. Bandwidth constrained mobile devices use on-demand approach in their routing protocols because of its effectiveness and efficiency. Many researchers have conducted numerous simulations for comparing the performance of these protocols under varying conditions and constraints. Most of them are not aware of MAC Protocols, which will impact the relative performance of routing protocols considered in different network scenarios. In this paper we investigate the choice of MAC protocols affects the relative performance of ad hoc routing protocols under different scenarios. We have evaluated the performance of these protocols using NS2 simulations. Our results show that the performance of routing protocols of ad hoc networks will suffer when run over different MAC Layer protocols.

Keywords: AODV, DSR, DSDV, MAC, MANETs, relativeperformance

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Authors: Zarita Zainuddin, Ong Pauline

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Wavelet neural networks (WNNs) have emerged as a vital alternative to the vastly studied multilayer perceptrons (MLPs) since its first implementation. In this paper, we applied various clustering algorithms, namely, K-means (KM), Fuzzy C-means (FCM), symmetry-based K-means (SBKM), symmetry-based Fuzzy C-means (SBFCM) and modified point symmetry-based K-means (MPKM) clustering algorithms in choosing the translation parameter of a WNN. These modified WNNs are further applied to the heterogeneous cancer classification using benchmark microarray data and were compared against the conventional WNN with random initialization method. Experimental results showed that a WNN classifier with the MPKM algorithm is more precise than the conventional WNN as well as the WNNs with other clustering algorithms.

Keywords: Clustering, microarray, symmetry, wavelet neural networks.

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Authors: J. McCullagh, T. Whitfort

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Artificial Neural Networks (ANNs) have been used successfully in many scientific, industrial and business domains as a method for extracting knowledge from vast amounts of data. However the use of ANN techniques in the sporting domain has been limited. In professional sport, data is stored on many aspects of teams, games, training and players. Sporting organisations have begun to realise that there is a wealth of untapped knowledge contained in the data and there is great interest in techniques to utilise this data. This study will use player data from the elite Australian Football League (AFL) competition to train and test ANNs with the aim to predict the onset of injuries. The results demonstrate that an accuracy of 82.9% was achieved by the ANNs’ predictions across all examples with 94.5% of all injuries correctly predicted. These initial findings suggest that ANNs may have the potential to assist sporting clubs in the prediction of injuries.

Keywords: Artificial Neural Networks, data, injuries, sport

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Authors: Ahmed Chalak Shakir, GuXuemai, Jia Min

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Recently, wireless sensor networks have been paid more interest, are widely used in a lot of commercial and military applications, and may be deployed in critical scenarios (e.g. when a malfunctioning network results in danger to human life or great financial loss). Such networks must be protected against human intrusion by using the secret keys to encrypt the exchange messages between communicating nodes. Both the symmetric and asymmetric methods have their own drawbacks for use in key management. Thus, we avoid the weakness of these two cryptosystems and make use of their advantages to establish a secure environment by developing the new method for encryption depending on the idea of code conversion. The code conversion-s equations are used as the key for designing the proposed system based on the basics of logic gate-s principals. Using our security architecture, we show how to reduce significant attacks on wireless sensor networks.

Keywords: logic gates, code conversions, Gray-code, and clustering.

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Authors: H. Bouhamed, A. Rebai, T. Lecroq, M. Jaoua

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The objective of our work is to develop a new approach for discovering knowledge from a large mass of data, the result of applying this approach will be an expert system that will serve as diagnostic tools of a phenomenon related to a huge information system. We first recall the general problem of learning Bayesian network structure from data and suggest a solution for optimizing the complexity by using organizational and optimization methods of data. Afterward we proposed a new heuristic of learning a Multi-Entities Bayesian Networks structures. We have applied our approach to biological facts concerning hereditary complex illnesses where the literatures in biology identify the responsible variables for those diseases. Finally we conclude on the limits arched by this work.

Keywords: Data-organization, data-optimization, automatic knowledge discovery, Multi-Entities Bayesian networks, score merging.

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Authors: S. Arun Rajan, S. Bhavani

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Wireless sensor networks can be tiny, low cost, intelligent sensors connected with advanced communication systems. WSNs have pulled in significant consideration as a matter of fact that, industrial as well as medical solicitations employ these in monitoring targets, conservational observation, obstacle exposure, movement regulator etc. In these applications, sensor hubs are thickly sent in the unattended environment with little non-rechargeable batteries. This constraint requires energy-efficient systems to drag out the system lifetime. There are redundancies in data sent over the network. To overcome this, multiple virtual spine scheduling has been presented. Such networks problems are called Maximum Lifetime Backbone Scheduling (MLBS) problems. Though this sleep wake cycle reduces radio usage, improvement can be made in the path in which the group heads stay selected. Cluster head selection with emphasis on geometrical relation of the system will enhance the load sharing among the nodes. Also the data are analyzed to reduce redundant transmission. Multi-hop communication will facilitate lighter loads on the network.

Keywords: WSN, wireless sensor networks, MLBS, maximum lifetime backbone scheduling.

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Authors: Khaja Kamaluddin, Muhammed Yousoof

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Available Bit Rate Service (ABR) is the lower priority service and the better service for the transmission of data. On wireline ATM networks ABR source is always getting the feedback from switches about increase or decrease of bandwidth according to the changing network conditions and minimum bandwidth is guaranteed. In wireless networks guaranteeing the minimum bandwidth is really a challenging task as the source is always in mobile and traveling from one cell to another cell. Re establishment of virtual circuits from start to end every time causes the delay in transmission. In our proposed solution we proposed the mechanism to provide more available bandwidth to the ABR source by re-usage of part of old Virtual Channels and establishing the new ones. We want the ABR source to transmit the data continuously (non-stop) inorderto avoid the delay. In worst case scenario at least minimum bandwidth is to be allocated. In order to keep the data flow continuously, priority is given to the handoff ABR call against new ABR call.

Keywords: Bandwidth allocation, Virtual Channel (VC), CBR, ABR, MCR and QOS.

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Authors: Mahdi Hayatdavudi, Ali Reza Rajabi, Mohammad Hassan Raouf, Mojtaba Saeedimoghadam, Amir Habibi

Abstract:

Generally, distributed generation units refer to small-scale electric power generators that produce electricity at a site close to the customer or an electric distribution system (in parallel mode). From the customers’ point of view, a potentially lower cost, higher service reliability, high power quality, increased energy efficiency, and energy independence can be the key points of a proper DG unit. Moreover, the use of renewable types of distributed generations such as wind, photovoltaic, geothermal or hydroelectric power can also provide significant environmental benefits. Therefore, it is of crucial importance to study their impacts on the distribution networks. A marked increase in Distributed Generation (DG), associated with medium voltage distribution networks, may be expected. Nowadays, distribution networks are planned for unidirectional power flows that are peculiar to passive systems, and voltage control is carried out exclusively by varying the tap position of the HV/MV transformer. This paper will compare different DG control methods and possible network reconfiguration aimed at assessing their effect on voltage profiles.

Keywords: Distribution Feeder Reconfiguration (DFR), Distributed Generator (DG), Voltage Profile, Control.

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Authors: R. Schweighoffer, N. Nagy, E. Reeves, B. Liebig

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Due to aging populations, the need for seamless palliative care provision is of central interest for western societies. An essential aspect of palliative care delivery is the quality of collaboration amongst palliative care providers. Therefore, the current research is based on Bainbridge’s conceptual framework, which provides an outline for the evaluation of palliative care provision. This study is the first one to investigate the predictive validity of spatial distribution on the quantity of interaction amongst various palliative care providers. Furthermore, based on the familiarity principle, we examine whether the extent of collaboration influences the perceived quality of collaboration among palliative care providers in urban versus rural areas of Switzerland. Based on a population-representative survey of Swiss palliative care providers, the results of the current study show that professionals in densely populated areas report higher absolute numbers of interactions and are more satisfied with their collaborative practice. This indicates that palliative care providers who work in urban areas are better embedded into networks than their counterparts in more rural areas. The findings are especially important, considering that efficient collaboration is a prerequisite to achieve satisfactory patient outcomes. Conclusively, measures should be taken to foster collaboration in weakly interconnected palliative care networks.

Keywords: Collaboration, healthcare networks, palliative care, Switzerland.

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Authors: I. López , J. Garay, R. Carreño, Z. Varga

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Monitoring of ecological systems is one of the major issues in ecosystem research. The concepts and methodology of mathematical systems theory provide useful tools to face this problem. In many cases, state monitoring of a complex ecological system consists in observation (measurement) of certain state variables, and the whole state process has to be determined from the observed data. The solution proposed in the paper is the design of an observer system, which makes it possible to approximately recover the state process from its partial observation. The method is illustrated with a trophic chain of resource – producer – primary consumer type and a numerical example is also presented.

Keywords: Monitoring, observer system, trophic chain

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Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

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2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: Artificial Intelligence, machine learning, deep learning, convolutional neural networks.

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Authors: Raquel M. de Sousa, Sofiane Labidi, Allan Kardec D. Barros, Alex O. Barradas Filho, Aldalea L. B. Marques

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Several parameters are established in order to measure biodiesel quality. One of them is the iodine value, which is an important parameter that measures the total unsaturation within a mixture of fatty acids. Limitation of unsaturated fatty acids is necessary since warming of higher quantity of these ones ends in either formation of deposits inside the motor or damage of lubricant. Determination of iodine value by official procedure tends to be very laborious, with high costs and toxicity of the reagents, this study uses artificial neural network (ANN) in order to predict the iodine value property as an alternative to these problems. The methodology of development of networks used 13 esters of fatty acids in the input with convergence algorithms of back propagation of back propagation type were optimized in order to get an architecture of prediction of iodine value. This study allowed us to demonstrate the neural networks’ ability to learn the correlation between biodiesel quality properties, in this caseiodine value, and the molecular structures that make it up. The model developed in the study reached a correlation coefficient (R) of 0.99 for both network validation and network simulation, with Levenberg-Maquardt algorithm.

Keywords: Artificial Neural Networks, Biodiesel, Iodine Value, Prediction.

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Authors: S. M. Kamruzzaman

Abstract:

In this paper, we propose a dynamic TDMA slot reservation (DTSR) protocol for cognitive radio ad hoc networks. Quality of Service (QoS) guarantee plays a critically important role in such networks. We consider the problem of providing QoS guarantee to users as well as to maintain the most efficient use of scarce bandwidth resources. According to one hop neighboring information and the bandwidth requirement, our proposed protocol dynamically changes the frame length and the transmission schedule. A dynamic frame length expansion and shrinking scheme that controls the excessive increase of unassigned slots has been proposed. This method efficiently utilizes the channel bandwidth by assigning unused slots to new neighboring nodes and increasing the frame length when the number of slots in the frame is insufficient to support the neighboring nodes. It also shrinks the frame length when half of the slots in the frame of a node are empty. An efficient slot reservation protocol not only guarantees successful data transmissions without collisions but also enhance channel spatial reuse to maximize the system throughput. Our proposed scheme, which provides both QoS guarantee and efficient resource utilization, be employed to optimize the channel spatial reuse and maximize the system throughput. Extensive simulation results show that the proposed mechanism achieves desirable performance in multichannel multi-rate cognitive radio ad hoc networks.

Keywords: TDMA, cognitive radio, ad hoc networks, QoSguarantee, dynamic frame length.

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Authors: Jun Seok Kim, Cheong-Sool Park, Jun-Geol Baek, Sung-Shick Kim

Abstract:

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.

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Authors: Oguz Ustun, Erdal Bekiroglu

Abstract:

In the present study, position estimation of switched reluctance motor (SRM) has been achieved on the basis of the artificial neural networks (ANNs). The ANNs can estimate the rotor position without using an extra rotor position sensor by measuring the phase flux linkages and phase currents. Flux linkage-phase current-rotor position data set and supervised backpropagation learning algorithm are used in training of the ANN based position estimator. A 4-phase SRM have been used to verify the accuracy and feasibility of the proposed position estimator. Simulation results show that the proposed position estimator gives precise and accurate position estimations for both under the low and high level reference speeds of the SRM

Keywords: Artificial neural networks, modeling andsimulation, position observer, switched reluctance motor.

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Authors: J. Silvestre, L. Almeida, R. Marau, P. Pedreiras

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Currently, there are many local area industrial networks that can give guaranteed bandwidth to synchronous traffic, particularly providing CBR channels (Constant Bit Rate), which allow improved bandwidth management. Some of such networks operate over Ethernet, delivering channels with enough capacity, specially with compressors, to integrate multimedia traffic in industrial monitoring and image processing applications with many sources. In these industrial environments where a low latency is an essential requirement, JPEG is an adequate compressing technique but it generates VBR traffic (Variable Bit Rate). Transmitting VBR traffic in CBR channels is inefficient and current solutions to this problem significantly increase the latency or further degrade the quality. In this paper an R(q) model is used which allows on-line calculation of the JPEG quantification factor. We obtained increased quality, a lower requirement for the CBR channel with reduced number of discarded frames along with better use of the channel bandwidth.

Keywords: Industrial Networks, Multimedia.

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Authors: Seyed Ali Sadat Noori, Elham Sahebi Bazaz

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Topological changes in mobile ad hoc networks frequently render routing paths unusable. Such recurrent path failures have detrimental effects on quality of service. A suitable technique for eliminating this problem is to use multiple backup paths between the source and the destination in the network. This paper proposes an effective and efficient protocol for backup and disjoint path set in ad hoc wireless network. This protocol converges to a highly reliable path set very fast with no message exchange overhead. The paths selection according to this algorithm is beneficial for mobile ad hoc networks, since it produce a set of backup paths with more high reliability. Simulation experiments are conducted to evaluate the performance of our algorithm in terms of route numbers in the path set and its reliability. In order to acquire link reliability estimates, we use link expiration time (LET) between two nodes.

Keywords: Wireless Ad Hoc Networks, Reliability, Routing, Disjoint Path

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Authors: M. S. Anton Merkulov

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This article discusses the questions concerning of creating small packet networks for energy companies with application of high voltage power line carrier equipment (PLC) with functionality of IP traffic transmission. The main idea is to create converged PLC links between substations and dispatching centers where packet data and voice are transmitted in one data flow. The article contents description of basic conception of the network, evaluation of voice traffic transmission parameters, and discussion of header compression techniques in relation to PLC links. The results of exploration show us, that convergent packet PLC links can be very useful in the construction of small packet networks between substations in remote locations, such as deposits or low populated areas.

Keywords: packet PLC, VoIP, time delay, packet traffic, overhead compression

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Authors: A. Pashayev, C. Ardil, R. Sadiqov

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Various methods of geofield parameters restoration (by algebraic polynoms; filters; rational fractions; interpolation splines; geostatistical methods – kriging; search methods of nearest points – inverse distance, minimum curvature, local – polynomial interpolation; neural networks) have been analyzed and some possible mistakes arising during geofield surface modeling have been presented.

Keywords: interpolation methods, geofield parameters, neural networks.

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