Search results for: wireless network

Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: Andrey Lebedev, Ilya Dmitrenok, JooYoung Lee, Leonard Johard

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Top-k shortest path routing problem is an extension of finding the shortest path in a given network. Shortest path is one of the most essential measures as it reveals the relations between two nodes in a network. However, in many real world networks, whose diameters are small, top-k shortest path is more interesting as it contains more information about the network topology. Many variations to compute top-k shortest paths have been studied. In this paper, we apply an efficient top-k shortest distance routing algorithm to the link prediction problem and test its efficacy. We compare the results with other base line and state-of-the-art methods as well as with the shortest path. Then, we also propose a top-k distance based graph matching algorithm.

Keywords: graph matching, link prediction, shortest path, similarity

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Authors: Zhen Wu, David Lupien St-Pierre, Georges Abdul-Nour

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In decision making under strict uncertainty, decision makers have to choose a decision without any information about the states of nature. The classic criteria of Laplace, Wald, Savage, Hurwicz and Starr are introduced and compared in a case study of sewer network planning. Furthermore, results from different criteria are discussed and analyzed. Moreover, this paper discusses the idea that decision making under strict uncertainty (DMUSU) can be viewed as a two-player game and thus be solved by a solution concept in game theory: Nash equilibrium.

Keywords: decision criteria, decision making, sewer network planning, decision making, strict uncertainty

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to preventing deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network.

Keywords: accident risks estimation, artificial neural network, deep learning, k-mean, road safety

Procedia PDF Downloads 157

Authors: K. Iskandar, N. H. Radzi, R. Aziz, M. S. Kamaruddin, M. N. Abdullah, S. A. Jumaat

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Nowadays many developing countries have been undergoing a restructuring process in the power electricity industry. This process has involved disaggregating former state-owned monopoly utilities both vertically and horizontally and introduced competition. The restructuring process has been implemented by the Australian National Electricity Market (NEM) started from 13 December 1998, began operating as a wholesale market for supply of electricity to retailers and end-users in Queensland, New South Wales, the Australian Capital Territory, Victoria and South Australia. In this deregulated market, one of the important issues is the transmission pricing. Transmission pricing is a service that recovers existing and new cost of the transmission system. The regulation of the transmission pricing is important in determining whether the transmission service system is economically beneficial to both side of the users and utilities. Therefore, an efficient transmission pricing methodology plays an important role in the Australian NEM. In this paper, the transmission pricing methodologies that have been implemented by the Australian NEM which are the Cost Reflective Network Pricing (CRNP) and Modified Cost Reflective Network Pricing (MCRNP) methods are investigated for allocating the transmission service charges to the transmission users. A case study using 6-bus system is used in order to identify the best method that reflects a fair and equitable transmission service charge.

Keywords: cost-reflective network pricing method, modified cost-reflective network pricing method, restructuring process, transmission pricing

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Authors: Chejarla Raghunathababu, E. Logashanmugam

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An antenna is an assortment of linked devices that function together to transmit and receive radio waves as a single antenna. Antennas occur in a variety of sizes and forms, but the microstrip patch antenna outperforms other types in terms of effectiveness and prediction. These antennas are easy to generate with discreet benefits. Nevertheless, the antenna's effectiveness will be affected because of the patch's shape above a thick dielectric substrate. As a result, a double-pole rectangular microstrip antenna with nanofillers was suggested in this study. By employing nano-composite substances (Fumed Silica and Aluminum Oxide), which are composites of graphene with nanofillers, the physical characteristics of the microstrip antenna, that is, the elevation of the microstrip antenna substrate and the width of the patch microstrip antenna have been improved in this research. The surface conductivity of graphene may be modified to function at specific frequencies. In order to prepare for future wireless communication technologies, a microstrip patch antenna operating at 93 GHz resonant frequency is constructed and investigated. The goal of this study was to reduce VSWR and increase gain. The simulation yielded results for the gain and VSWR, which were 8.26 dBi and 1.01, respectively.

Keywords: graphene, microstrip patch antenna, substrate material, wireless communication, nanocomposite material

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Authors: Zan Azma Nasruddin, Irwan Mazlin, Nor Aziah Daud, Fauziah Redzuan, Fariza Hanis Abdul Razak

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This paper focuses on a machine learning that learn the correct pronunciation of Makhraj Huroofs. Usually, people need to find an expert to pronounce the Huroof accurately. In this study, the researchers have developed a system that is able to learn the selected Huroofs which are ha, tsa, zho, and dza using the Convolutional Neural Network. The researchers present the chosen type of the CNN architecture to make the system that is able to learn the data (Huroofs) as quick as possible and produces high accuracy during the prediction. The researchers have experimented the system to measure the accuracy and the cross entropy in the training process.

Keywords: convolutional neural network, Makhraj recognition, speech recognition, signal processing, tensorflow

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Authors: Michael K. O. Ayomoh, Oyindamola A. Omotuyi

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Autonomous mobile robots deployed to explore or operate in a never explored or an abandoned sub-surface mine requires extreme effectiveness in coordination and communication. In a bid to transmit information from the depth of the mine to the external surface in real-time and amidst diverse physical, chemical and virtual impediments, the concept of unified cooperative robots is seen to be a proficient approach. This paper presents an effective [human → robot → task] coordination framework for effective exploration of an abandoned underground mine. The problem addressed in this research is basically the development of a globalized optimization model premised on time series differentiation and geometrical configurations for effective positioning of the two classes of robots in the cooperation namely the outermost stationary master (OSM) robots and the innermost dynamic task (IDT) robots for effective bi-directional signal transmission. In addition, the synchronization of a vision system and wireless communication system for both categories of robots, fiber optics system for the OSM robots in cases of highly sloppy or vertical mine channels and an autonomous battery recharging capability for the IDT robots further enhanced the proposed concept. The OSM robots are the master robots which are positioned at strategic locations starting from the mine open surface down to its base using a fiber-optic cable or a wireless communication medium all subject to the identified mine geometrical configuration. The OSM robots are usually stationary and function by coordinating the transmission of signals from the IDT robots at the base of the mine to the surface and in a reverse order based on human decisions at the surface control station. The proposed scheme also presents an optimized number of robots required to form the cooperation in a bid to reduce overall operational cost and system complexity.

Keywords: sub-surface mine, wireless communication, outermost stationary master robots, inner-most dynamic robots, fiber optic

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Authors: Sirilak Areerachakul

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Water quality has initiated serious management efforts in many countries. Artificial Neural Network (ANN) models are developed as forecasting tools in predicting water quality trend based on historical data. This study endeavors to automatically classify water quality. The water quality classes are evaluated using 6 factor indices. These factors are pH value (pH), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen (NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (T-Coliform). The methodology involves applying data mining techniques using multilayer perceptron (MLP) neural network models. The data consisted of 11 sites of Saen Saep canal in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2007-2011. The results of multilayer perceptron neural network exhibit a high accuracy multilayer perception rate at 94.23% in classifying the water quality of Saen Saep canal in Bangkok. Subsequently, this encouraging result could be combined with GIS data improves the classification accuracy significantly.

Keywords: artificial neural network, geographic information system, water quality, computer science

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Authors: Zongyan Li, Matt Best

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This paper presents an optimization method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilizing the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. An application of vehicle dynamic model identification is also presented in this paper to demonstrate the optimization technique and the optimal input layer structure and the optimal number of neurons for the neural network is investigated.

Keywords: correlation analysis, F-ratio, levenberg-marquardt, MSE, NARX, neural network, optimisation

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Authors: Chia-Feng Lu, Yuh-Jen Wang, Yu-Te Wu, Sui-Hing Yan

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This study aimed at investigating whether the functional brain networks constructed using the initial EEG (obtained when patients first visited hospital) can be correlated with the progression of cognitive decline calculated as the changes of mini-mental state examination (MMSE) scores between the latest and initial examinations. We integrated the time–frequency cross mutual information (TFCMI) method to estimate the EEG functional connectivity between cortical regions, and the network analysis based on graph theory to investigate the organization of functional networks in aMCI. Our finding suggested that higher integrated functional network with sufficient connection strengths, dense connection between local regions, and high network efficiency in processing information at the initial stage may result in a better prognosis of the subsequent cognitive functions for aMCI. In conclusion, the functional connectivity can be a useful biomarker to assist in prediction of cognitive declines in aMCI.

Keywords: cognitive decline, functional connectivity, MCI, MMSE

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Authors: Wiseborn Manfe Danquah, D. Turgay Altilar

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Vehicular Ad-hoc Network (VANET) is an integral component of Intelligent Transport Systems (ITS) that has enjoyed a lot of attention from the research community and the automotive industry. This is mainly due to the opportunities and challenges it presents. Vehicular Ad-hoc Network being a class of Mobile Ad-hoc Networks (MANET) has all the security concerns existing in traditional MANET as well as new security and privacy concerns introduced by the unique vehicular communication environment. This paper provides a survey of the possible attacks in vehicular environment, as well as security and privacy concerns in VANET. It also provides an insight into the development of a comprehensive cloud framework to provide a more robust and secured communication among vehicular nodes and road side units. Our proposal, a Metropolitan Based Public Interconnected Vehicular Cloud (MIVC) infrastructure seeks to provide a more reliable and secured vehicular communication network.

Keywords: mobile Ad-hoc networks, vehicular ad hoc network, cloud, ITS, road side units (RSU), metropolitan interconnected vehicular cloud (MIVC)

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Authors: Xudong He, Jian Wang, Jiqiang Liu, Lei Han, Yang Yu, Shaohua Lv

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Nowadays, the threats of the internet are enormous and increasing; however, the classification of huge alert messages generated in this environment is relatively monotonous. It affects the accuracy of the network situation assessment, and also brings inconvenience to the security managers to deal with the emergency. In order to deal with potential network threats effectively and provide more effective data to improve the network situation awareness. It is essential to build a hierarchical filtering method to prevent the threats. In this paper, it establishes a model for data monitoring, which can filter systematically from the original data to get the grade of threats and be stored for using again. Firstly, it filters the vulnerable resources, open ports of host devices and services. Then use the entropy theory to calculate the performance changes of the host devices at the time of the threat occurring and filter again. At last, sort the changes of the performance value at the time of threat occurring. Use the alerts and performance data collected in the real network environment to evaluate and analyze. The comparative experimental analysis shows that the threat filtering method can effectively filter the threat alerts effectively.

Keywords: correlation analysis, hierarchical filtering, multisource data, network security

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Authors: El Miloudi Djelloul

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The computer network play an important position for practical implementations of the differently system. To implement a system into network above all is needed to know all the configurations, which is responsible to be a part of the system, and to give adequate information and solution in realtime. So if want to implement this system for example in the school or relevant institutions, the first step is to analyze the types of model which is needed to be configured and another important step is to organize the works in the context of devices, as a part of the general system. Often before configuration, as important point is descriptions and documentations from all the works into the respective process, and then to organize in the aspect of problem-solving. The computer network as critic infrastructure is very specific so the paper present the effectiveness solutions in the structured aspect viewed from one side, and another side is, than the paper reflect the positive aspect in the context of modeling and block schema presentations as an better alternative to solve the specific problem because of continually distortions of the system from the line of devices, programs and signals or packed collisions, which are in movement from one computer node to another nodes.

Keywords: local area networks, LANs, block schema presentations, computer network system, computer node, critical infrastructure packed collisions, structural control system representations, computer network, implementations, modeling structural representations, companies, computers, context, control systems, internet, software

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Authors: Shreyansh Adesara, Sneha Pandiya

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The mobile ad-hoc network (MANET) is an important and open area research for the examination and determination of the performance evolution. Temporary ordered routing algorithm (TORA) is adaptable and distributed MANET routing algorithm which is totally dependent on internet MANET Encapsulation protocol (IMEP) for the detection of the link and sensing of the link. If IMEP detect the wrong link failure then the network suffer from congestion and unnecessary route maintenance. Thus, the improvement in link detection method of TORA is introduced by various methods on IMEP by different perspective from different person. There are also different reactive routing protocols like AODV, TORA and DSR has been compared for the knowledge of the routing scenario for different parameter and using different model.

Keywords: IMEP, mobile ad-hoc network, protocol, TORA

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Authors: Michel Berthiaume, Daniel Chamberland-Tremblay, Elaine Paiva Mosconi, Jérôme Blanchet-Brisson

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This presentation documents the overall failure of North-American universities to build an effective IT Policies communication with their primary users: the students. A sample of 12 universities was selected. A set of indicators based on usability principles to assess the content of IT Policies vas devised. Then, IT Policies were rated according to the indicators and the results analyzed to build an overall picture of the potential of communication problems in policy communication. The initial finding is that network security professionals in Universities have to reach a delicate balance between asset protection, asset valorization and user security awareness.

Keywords: computer security, IT policy, security awareness, network user rules

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Authors: Mustafa Al Sukar, Azzam Sleit, Abdullatif Abu-Dalhoum, Bassam Al-Kasasbeh

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Use and abuse of drugs by teens is very common and can have dangerous consequences. The drugs contribute to physical and sexual aggression such as assault or rape. Some teenagers regularly use drugs to compensate for depression, anxiety or a lack of positive social skills. Teen resort to smoking should not be minimized because it can be "gateway drugs" for other drugs (marijuana, cocaine, hallucinogens, inhalants, and heroin). The combination of teenagers' curiosity, risk taking behavior, and social pressure make it very difficult to say no. This leads most teenagers to the questions: "Will it hurt to try once?" Nowadays, technological advances are changing our lives very rapidly and adding a lot of technologies that help us to track the risk of drug abuse such as smart phones, Wireless Sensor Networks (WSNs), Internet of Things (IoT), etc. This technique may help us to early discovery of drug abuse in order to prevent an aggravation of the influence of drugs on the abuser. In this paper, we have developed a Decision Support System (DSS) for detecting the drug abuse using Artificial Neural Network (ANN); we used a Multilayer Perceptron (MLP) feed-forward neural network in developing the system. The input layer includes 50 variables while the output layer contains one neuron which indicates whether the person is a drug addict. An iterative process is used to determine the number of hidden layers and the number of neurons in each one. We used multiple experiment models that have been completed with Log-Sigmoid transfer function. Particularly, 10-fold cross validation schemes are used to access the generalization of the proposed system. The experiment results have obtained 98.42% classification accuracy for correct diagnosis in our system. The data had been taken from 184 cases in Jordan according to a set of questions compiled from Specialists, and data have been obtained through the families of drug abusers.

Keywords: drug addiction, artificial neural networks, multilayer perceptron (MLP), decision support system

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Authors: I. Baddari, A. Riahla, M. Mezghich

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Today in the literature, we discover a lot of routing algorithms which some have been the subject of normalization. Two great classes Routing algorithms are defined, the first is the class reactive algorithms and the second that of algorithms proactive. The aim of this work is to make a comparative study between some routing algorithms. Two comparisons are considered. The first will focus on the protocols of the same class and second class on algorithms of different classes (one reactive and the other proactive). Since they are not based on analytical models, the exact evaluation of some aspects of these protocols is challenging. Simulations have to be done in order to study their performances. Our simulation is performed in NS2 (Network Simulator 2). It identified a classification of the different routing algorithms studied in a metrics such as loss of message, the time transmission, mobility, etc.

Keywords: ad-hoc network routing protocol, simulation, NS2, delay, packet loss, wideband, mobility

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Authors: Sandip Sadhukhan, Arpita Sarkar, Debprasad Sinha, Goutam Kumar Ghorai, Gautam Sarkar, Ashis K. Dhara

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Accurate segmentation of the optic disc is very important for computer-aided diagnosis of several ocular diseases such as glaucoma, diabetic retinopathy, and hypertensive retinopathy. The paper presents an accurate and fast optic disc detection and segmentation method using an attention based fully convolutional network. The network is trained from scratch using the fundus images of extended MESSIDOR database and the trained model is used for segmentation of optic disc. The false positives are removed based on morphological operation and shape features. The result is evaluated using three-fold cross-validation on six public fundus image databases such as DIARETDB0, DIARETDB1, DRIVE, AV-INSPIRE, CHASE DB1 and MESSIDOR. The attention based fully convolutional network is robust and effective for detection and segmentation of optic disc in the images affected by diabetic retinopathy and it outperforms existing techniques.

Keywords: attention-based fully convolutional network, optic disc detection and segmentation, retinal fundus image, screening of ocular diseases

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Authors: H. Parveen Begam, M. A. Maluk Mohamed

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Grid computing is an environment that allows sharing and coordinated use of diverse resources in dynamic, heterogeneous and distributed environment using Virtual Organization (VO). Security is a critical issue due to the open nature of the wireless channels in the grid computing which requires three fundamental services: authentication, authorization, and encryption. The privacy and anonymity are considered as an important factor while communicating over publicly spanned network like web. To ensure a high level of security we explored an extension of onion routing, which has been used with dynamic token exchange along with protection of privacy and anonymity of individual identity. To improve the performance of encrypting the layers, the elliptic curve cryptography is used. Compared to traditional cryptosystems like RSA (Rivest-Shamir-Adelman), ECC (Elliptic Curve Cryptosystem) offers equivalent security with smaller key sizes which result in faster computations, lower power consumption, as well as memory and bandwidth savings. This paper presents the estimation of the performance improvements of onion routing using ECC as well as the comparison graph between performance level of RSA and ECC.

Keywords: grid computing, privacy, anonymity, onion routing, ECC, RSA

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Authors: Yang Yang, Dan Liu

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Anomaly detection models for network flow based on machine learning have poor detection performance under extremely unbalanced training data conditions and also have slow detection speed and large resource consumption when deploying on network edge devices. Embedding multi-teacher knowledge distillation (mKD) in anomaly detection can transfer knowledge from multiple teacher models to a single model. Inspired by this, we proposed a state-of-the-art model, mKDNAD, to improve detection performance. mKDNAD mine and integrate the knowledge of one-dimensional sequence and two-dimensional image implicit in network flow to improve the detection accuracy of small sample classes. The multi-teacher knowledge distillation method guides the train of the student model, thus speeding up the model's detection speed and reducing the number of model parameters. Experiments in the CICIDS2017 dataset verify the improvements of our method in the detection speed and the detection accuracy in dealing with the small sample classes.

Keywords: network flow anomaly detection (NAD), multi-teacher knowledge distillation, machine learning, deep learning

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Authors: Ahmed Alutaibi, Ganti Sudhakar

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Providing better Quality-of-Service (QoS) to end users has been a challenging problem for researchers and service providers. Building applications relying on best effort network protocols hindered the adoption of guaranteed service parameters and, ultimately, Quality of Service. The introduction of Software Defined Networking (SDN) opened the door for a new paradigm shift towards a more controlled programmable configurable behavior. Openflow has been and still is the main implementation of the SDN vision. To facilitate better QoS for applications, the network must calculate and measure certain parameters. One of those parameters is the delay between the two ends of the connection. Using the power of SDN and the knowledge of application and network behavior, SDN networks can adjust to different conditions and specifications. In this paper, we use the capabilities of SDN to implement multiple algorithms to measure delay end-to-end not only inside the SDN network. The results of applying the algorithms on an emulated environment show that we can get measurements close to the emulated delay. The results also show that depending on the algorithm, load on the network and controller can differ. In addition, the transport layer handshake algorithm performs best among the tested algorithms. Out of the results and implementation, we show the limitations of Openflow and develop suggestions to solve them.

Keywords: software defined networking, quality of service, delay measurement, openflow, mininet

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Authors: Sung Hyun You, Jeong Hoon Kim, Dae Ki Kim, Choon Ki Ahn

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In this paper, we proposed a new strategy for estimating indoor temperature based on the modified resistance capacitance (R–C) network thermal dynamic model. Using minimum variance finite impulse response (FIR) filter, accurate indoor temperature estimation can be achieved. Our study is clarified by the experimental validation of the proposed indoor temperature estimation method. This experiment scenario environment is composed of a demand response (DR) server and home energy management system (HEMS) in a test bed.

Keywords: energy consumption, resistance-capacitance network model, demand response, finite impulse response filter

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Authors: Wisal Alrabadi

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The study's goal was to see how the use of water wireless Aqua Com System and its accompanying music affected the Female Swimming Team at the Faculty of Physical Education's development of dolphin kick movements. To that end, a training program consisting of (12) training units spread out over four weeks, three units per week, was created and applied to a study sample of (10) students from the swimming pool enrolled in the first semester of the academic year 2022. Pre-measuring and timing the movements of dolphins kicking with and without fins above and below, measuring the water's surface over a distance of 25 meters. The results showed that there are statistically significant differences in favor of telemetry from the start within the limits of the area specified for a distance of 15 m after the comparison between the pre and post-measurement using the test (T) of the double samples, and this indicates the impact of the training program using the Aqua Com System in the swimming team(Female) at Faculty of Physical Education, and in light of this a set of recommendations was developed.

Keywords: aqua com system training program, accompanying music, dolphin kick movements, swimming team female

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Authors: Hyun Joon Chang, Jae In Kim, Sungsoo Na

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Kinesin-1 (hereafter called kinesin) is a molecular motor protein that moves cargos toward the end of microtubules using the energy of adenosine triphosphate (ATP) hydrolysis. When kinesin is inactive, its tail autoinhibits the motor chain in order to prevent from reacting with the ATP by cross-linking of the tail domain to the motor domains at two positions. However, the morphological study of kinesin during autoinhibition is yet remained obscured. In this study, we report the effect of the binding site of the tail domain using the normal mode analysis of the elastic network model on kinesin in the tail-free form and tail-bind form. Considering the relationship between the connectivity of conventional network model with respect to the cutoff length and the functionality of the binding site of the tail, we revaluated the network model to observe the key role of the tail domain in its structural aspect. Contingent on the existence of the tail domain, the results suggest the morphological stability of the motor domain. Furthermore, employing the results from normal mode analysis, we have determined the strain energy of the neck linker, an essential portion of the motor domain for ATP hydrolysis. The results of the neck linker also converge to the same indication, i.e. the morphological analysis of the motor domain.

Keywords: elastic network model, Kinesin-1, autoinhibition

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Authors: Naveed Ghani

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With the arrival of higher speed communication links and mature application running over the internet, the requirement for reliable, efficient and robust network designs rising day by day. Multi-Protocol Label Switching technology (MPLS) Virtual Private Networks (VPNs) have committed to provide optimal network services. They are gaining popularity in industry day by day. Enterprise customers are moving to service providers that offer MPLS VPNs. The main reason for this shifting is the capability of MPLS VPN to provide built in security features and any-to-any connectivity. MPLS VPNs improved the network performance due to fast label switching as compare to traditional IP Forwarding but traffic classification and policing was still required on per hop basis to enhance the performance of real time traffic which is delay sensitive (particularly voice and video). QoS (Quality of service) is the most important factor to prioritize enterprise networks’ real time traffic such as voice and video. This thesis is focused on the study of QoS parameters (e.g. delay, jitter and MOS (Mean Opinion Score)) for the real time traffic over MPLS VPNs. DiffServ (Differentiated Services) QoS model will be used over MPLS VPN network to get end-to-end service quality.

Keywords: network, MPLS, VPN, DiffServ, MPLS VPN, DiffServ QoS, QoS Model, GNS2

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Authors: Emmanuel D. Buedi, K. O. Boateng, Griffith S. Klogo

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Electrical distribution networks are prone to power quality disturbances originating from the complexity of the distribution network, mode of distribution (overhead or underground) and types of loads used by customers. Data on the types of disturbances present and frequency of occurrence is needed for economic evaluation and hence finding solution to the problem. Utility companies have resorted to using secondary power quality devices such as smart meters to help gather the required data. Even though this approach is easier to adopt, data gathered from these devices may not serve the required purpose, since the installation of these devices in the electrical network usually does not conform to available PQM placement methods. This paper presents a design of a PQM that is capable of integrating into an existing DAS infrastructure to take advantage of available placement methodologies. The monitoring component of the design is implemented and installed to monitor an existing LV network. Data from the monitor is analyzed and presented. A portion of the LV network of the Electricity Company of Ghana is modeled in MATLAB-Simulink and analyzed under various earth fault conditions. The results presented show the ability of the PQM to detect and analyze PQ disturbance such as voltage sag and overvoltage. By adopting a placement methodology and installing these nodes, utilities are assured of accurate and reliable information with respect to the quality of power delivered to consumers.

Keywords: power quality, remote monitoring, distributed automation system, economic evaluation, LV network

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Authors: Adham M. Salah, Tariq A. Nagem, Raed A. Abd-Alhameed, James M. Noras

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Recently, the demand for wireless communications systems to cover more than one frequency band (multi-band) with high data rate has been increased for both fixed and mobile services. Multiple Input Multiple Output (MIMO) technology is one of the significant solutions for attaining these requirements and to achieve the maximum channel capacity of the wireless communications systems. The main issue associated with MIMO antennas especially in portable devices is the compact space between the radiating elements which leads to limit the physical separation between them. This issue exacerbates the performance of the MIMO antennas by increasing the mutual coupling between the radiating elements. In other words, the mutual coupling will be stronger if the radiating elements of the MIMO antenna are closer. This paper presents a low–profile dual-band (2×1) MIMO antenna that works at 2.4GHz, 5.3GHz and 5.8GHz for wireless local area networks (WLAN) applications. A neutralization line (NL) technique for enhancing the isolation has been used by introducing a strip line with a length of λg/4 at the isolation frequency (2.4GHz) between the radiating elements. The overall dimensions of the antenna are 33.5 x 36 x 1.6 mm³. The fabricated prototype shows a good agreement between the simulated and measured results. The antenna impedance bandwidths are 2.38–2.75 GHz and 4.4–6 GHz for the lower and upper band respectively; the reflection coefficient and mutual coupling are better than -25 dB in both lower and higher bands. The MIMO antenna performance characteristics are reported in terms of the scattering parameters, envelope correlation coefficient (ECC), total active reflection coefficient, capacity loss, antenna gain, and radiation patterns. Analysis of these characteristics indicates that the design is appropriate for the WLAN terminal applications.

Keywords: ECC, neutralization line, MIMO antenna, multi-band, mutual coupling, WLAN

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Authors: Ayman Othman, Tallat Ali

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A pavement maintenance management system (PMMS) was developed for the city of Aswan as a model of a small city to provide the road maintenance department in Aswan city with the capabilities for comprehensive planning of the maintenance activities needed to put the internal pavement network into desired physical condition in view of maintenance budget constraints. The developed system consists of three main stages. First is the inventory & condition survey stage where the internal pavement network of Aswan city was inventoried and its actual conditions were rated in segments of 100 meters length. Second is the analysis stage where pavement condition index (PCI) was calculated and the most appropriate maintenance actions were assigned for each segment. The total maintenance budget was also estimated and a parameter based ranking criteria were developed to prioritize maintenance activities when the available maintenance budget is not sufficient. Finally comes the packaging stage where approved maintenance budget is packed into maintenance projects for field implementation. System results indicate that, the system output maintenance budget is very reasonable and the system output maintenance programs agree to a great extent with the actual maintenance needs of the network. Condition survey of Aswan city road network showed that roughness is the most dominate distress. In general, the road network can be considered in a fairly reasonable condition, however, the developed PMMS needs to be officially adapted to maintain the road network in a desirable condition and to prevent further deterioration.

Keywords: pavement, maintenance, management, system, distresses, survey, ranking

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Authors: Khader S. Al-Aidmat, Venus W. Samawi

Abstract:

Blurred vision in the misty atmosphere is essential problem which needs to be resolved. To solve this problem, we developed a technique to restore foggy degraded image from its original version using Back-propagation neural network (BP-NN). The suggested technique is based on mapping between foggy scene and its corresponding original scene. Seven different approaches are suggested based on type of features used in image restoration. Features are extracted from spatial and spatial-frequency domain (using DCT). Each of these approaches comes with its own BP-NN architecture depending on type and number of used features. The weight matrix resulted from training each BP-NN represents a fog filter. The performance of these filters are evaluated empirically (using PSNR), and perceptually. By comparing the performance of these filters, the effective features that suits BP-NN technique for restoring foggy images is recognized. This system proved its effectiveness and success in restoring moderate foggy images.

Keywords: artificial neural network, discrete cosine transform, feed forward neural network, foggy image restoration

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