Search results for: electric network theory

Authors: Elmabruk Laias, Abdalla M. Hanashi, Mohammed Alnas

Abstract:

Worldwide Interoperability for Microwave Access (WiMAX) became one of the most challenging issues, since it was responsible for distributing available resources of the network among all users this leaded to the demand of constructing and designing high efficient scheduling algorithms in order to improve the network utilization, to increase the network throughput, and to minimize the end-to-end delay. In this study, the proposed algorithm focuses on an efficient mechanism to serve non_real time traffic in congested networks by considering channel status.

Keywords: WiMAX, Quality of Services (QoS), OPNE, Diff-Serv (DS).

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Authors: András Barta, István Vajk

Abstract:

In pattern recognition applications the low level segmentation and the high level object recognition are generally considered as two separate steps. The paper presents a method that bridges the gap between the low and the high level object recognition. It is based on a Bayesian network representation and network propagation algorithm. At the low level it uses hierarchical structure of quadratic spline wavelet image bases. The method is demonstrated for a simple circuit diagram component identification problem.

Keywords: Object recognition, Bayesian network, Wavelets, Document processing.

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Authors: Axay J Mehta, Hema A Mehta, T.C.Manjunath, C. Ardil

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In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for power system. The dependence of load on several factors makes the load forecasting a very challenging job. An over estimation of the load may cause premature investment and unnecessary blocking of the capital where as under estimation of load may result in shortage of equipment and circuits. It is always better to plan the system for the load slightly higher than expected one so that no exigency may arise. In this paper, a load-forecasting model is proposed using a multilayer neural network with an appropriately modified back propagation learning algorithm. Once the neural network model is designed and trained, it can forecast the load of the power system 24 hours ahead on daily basis and can also forecast the cumulative load on daily basis. The real load data that is used for the Artificial Neural Network training was taken from LDC, Gujarat Electricity Board, Jambuva, Gujarat, India. The results show that the load forecasting of the ANN model follows the actual load pattern more accurately throughout the forecasted period.

Keywords: Power system, Load forecasting, Neural Network, Neuron, Stabilization, Network structure, Load.

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Authors: Muhammad Azam, Micheal Olaolu Arowolo, Fei He, Mihail Popescu, Dong Xu

Abstract:

The number of biological papers is growing quickly, which means that the number of biological pathway figures in those papers is also increasing quickly. Each pathway figure shows extensive biological information, like the names of genes and how the genes are related. However, manually annotating pathway figures takes a lot of time and work. Even though using advanced image understanding models could speed up the process of curation, these models still need to be made more accurate. To improve gene name recognition from pathway figures, we applied a Siamese network to map image segments to a library of pictures containing known genes in a similar way to person recognition from photos in many photo applications. We used a triple loss function and a triplet spatial pyramid pooling network by combining the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net). We compared VGG19 and VGG16 as the Siamese network model. VGG16 achieved better performance with an accuracy of 93%, which is much higher than Optical Character Recognition (OCR) results.

Keywords: Biological pathway, image understanding, gene name recognition, object detection, Siamese network, Visual Geometry Group.

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Authors: Jiajun Wang, Xiaoge Li

Abstract:

The purpose of the aspect-level sentiment analysis task is to identify the sentiment polarity of aspects in a sentence. Currently, most methods mainly focus on using neural networks and attention mechanisms to model the relationship between aspects and context, but they ignore the dependence of words in different ranges in the sentence, resulting in deviation when assigning relationship weight to other words other than aspect words. To solve these problems, we propose an aspect-level sentiment analysis model that combines a multi-channel convolutional network and graph convolutional network (GCN). Firstly, the context and the degree of association between words are characterized by Long Short-Term Memory (LSTM) and self-attention mechanism. Besides, a multi-channel convolutional network is used to extract the features of words in different ranges. Finally, a convolutional graph network is used to associate the node information of the dependency tree structure. We conduct experiments on four benchmark datasets. The experimental results are compared with those of other models, which shows that our model is better and more effective.

Keywords: Aspect-level sentiment analysis, attention, multi-channel convolution network, graph convolution network, dependency tree.

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Authors: Sanjoy Das, Akash Arya, Rishi Pal Singh

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Vehicular Ad hoc Network is basically the solution of several problems associated while vehicles are plying on the road. In this paper, we have focused on the detection of imposter node while it has stolen the ID's of the authenticated vehicle in the network. The purpose is to harm the network through imposter messages. Here, we have proposed a protocol namely Imposter Detection based on Location (IDBL), which will store the location coordinate of the each vehicle as the key of the authenticity of the message so that imposter node can be detected. The imposter nodes send messages from a stolen ID and show that it is from an authentic node ID. So, to detect this anomaly, the first location is checked and observed different from original vehicle location. This node is known as imposter node. We have implemented the algorithm through JAVA and tested various types of node distribution and observed the detection probability of imposter node.

Keywords: Authentication, detection, IDBL protocol, imposter node, node detection.

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Authors: C. Temaneh Nyah

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The analysis of electromagnetic environment using deterministic mathematical models is characterized by the impossibility of analyzing a large number of interacting network stations with a priori unknown parameters, and this is characteristic, for example, of mobile wireless communication networks. One of the tasks of the tools used in designing, planning and optimization of mobile wireless network is to carry out simulation of electromagnetic environment based on mathematical modelling methods, including computer experiment, and to estimate its effect on radio communication devices. This paper proposes the development of a statistical model of electromagnetic environment of a mobile wireless communication network by describing the parameters and factors affecting it including the propagation channel and their statistical models.

Keywords: Electromagnetic Environment, Statistical model, Wireless communication network.

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Authors: T. Lantharthong, N. Rugthaicharoencheep

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This paper presents an efficient algorithm for optimization of radial distribution systems by a network reconfiguration to balance feeder loads and eliminate overload conditions. The system load-balancing index is used to determine the loading conditions of the system and maximum system loading capacity. The index value has to be minimum in the optimal network reconfiguration of load balancing. A method based on Tabu search algorithm, The Tabu search algorithm is employed to search for the optimal network reconfiguration. The basic idea behind the search is a move from a current solution to its neighborhood by effectively utilizing a memory to provide an efficient search for optimality. It presents low computational effort and is able to find good quality configurations. Simulation results for a radial 69-bus system with distributed generations and capacitors placement. The study results show that the optimal on/off patterns of the switches can be identified to give the best network reconfiguration involving balancing of feeder loads while respecting all the constraints.

Keywords: Network reconfiguration, Distributed generation Capacitor placement, Load balancing, Optimization technique

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Authors: Chaudhuri Manoj Kumar Swain, Susmita Das

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This paper explores a detailed procedure of predicting a path loss (PL) model and its application in estimating the coverage probability in a WiMAX network. For this a hybrid approach is followed in predicting an empirical PL model of a 2.65 GHz WiMAX network deployed in a suburban environment. Data collection, statistical analysis, and regression analysis are the phases of operations incorporated in this approach and the importance of each of these phases has been discussed properly. The procedure of collecting data such as received signal strength indicator (RSSI) through experimental set up is demonstrated. From the collected data set, empirical PL and RSSI models are predicted with regression technique. Furthermore, with the aid of the predicted PL model, essential parameters such as PL exponent as well as the coverage probability of the network are evaluated. This research work may assist in the process of deployment and optimisation of any cellular network significantly.

Keywords: WiMAX, RSSI, path loss, coverage probability, regression analysis.

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Authors: H. Yazdani, K. Ghorbanian

Abstract:

This paper deals with modeling and simulation of the plasma actuator with OpenFOAM. Plasma actuator is one of the newest devices in flow control techniques which can delay separation by inducing external momentum to the boundary layer of the flow. The effects of the plasma actuators on the external flow are incorporated into Navier-Stokes computations as a body force vector which is obtained as a product of the net charge density and the electric field. In order to compute this body force vector, the model solves two equations: One for the electric field due to the applied AC voltage at the electrodes and the other for the charge density representing the ionized air. The simulation result is compared to the experimental and typical values which confirms the validity of the modeling.

Keywords: Active flow control, flow field, OpenFOAM, plasma actuator.

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Authors: Nahla Ibraheem Jabbar, Monica Mehrotra

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This paper used a fuzzy kohonen neural network for medical image segmentation. Image segmentation plays a important role in the many of medical imaging applications by automating or facilitating the diagnostic. The paper analyses the tumor by extraction of the features of (area, entropy, means and standard deviation).These measurements gives a description for a tumor.

Keywords: FCM, features extraction, medical image processing, neural network, segmentation.

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Authors: Wen-Shan Lin, Shu-Lu Hsu

Abstract:

Environmental concern and consumer rights have entailed e-tailers to adopt better strategies to facilitate product returns from customers. As the demand for reverse logistics (RL) continues to grow, little is known about what motivates e-tailers to enhance their RL capabilities and about the role RL capabilities plays in enabling e-tailers to achieve better customer satisfaction and economic performance. Based on resource-based theory and institutional theory, this article proposes that the following factors play a critical role in influencing the RL capability of e-tailers: (a) Financial resource commitment to RL, (b) managerial resource commitment to RL, and (c) institutional pressure to implement RL. Based on the role of these factors, the study provides a framework and propositions that serve to guide future research addressing the link among resources, institutional pressure, and RL capability.

Keywords: Reverse logistics, e-tailing, resource-based theory, institutional theory.

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Authors: Nasiri Ahmadullah, Shimozato Tetsuhiro, Masayuki Tai

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This paper presents the step-by-step procedure for using Elastic Theory to calculate the internal stresses in composite bridge girders prestressed by the Preflexing Technology, called Prebeam in Japan and Preflex beam worldwide. Elastic Theory approaches preflex beams the same way as it does the conventional composite girders. Since preflex beam undergoes different stages of construction, calculations are made using different sectional and material properties. Stresses are calculated in every stage using the properties of the specific section. Stress accumulation gives the available stress in a section of interest. Concrete presence in the section implies prestress loss due to creep and shrinkage, however; more work is required to be done in this field. In addition to the graphical presentation of this application, this paper further discusses important notes of graphical comparison between the results of an experimental-only research carried out on a preflex beam, with the results of simulation based on the elastic theory approach, for an identical beam using Finite Element Modeling (FEM) by the author.

Keywords: Composite girder, elastic theory, preflex beam, prestressing.

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Authors: Ashok Kumar Goel, Suresh Chandra Saxena, Surekha Bhanot

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In this work, a Modified Functional Link Artificial Neural Network (M-FLANN) is proposed which is simpler than a Multilayer Perceptron (MLP) and improves upon the universal approximation capability of Functional Link Artificial Neural Network (FLANN). MLP and its variants: Direct Linear Feedthrough Artificial Neural Network (DLFANN), FLANN and M-FLANN have been implemented to model a simulated Water Bath System and a Continually Stirred Tank Heater (CSTH). Their convergence speed and generalization ability have been compared. The networks have been tested for their interpolation and extrapolation capability using noise-free and noisy data. The results show that M-FLANN which is computationally cheap, performs better and has greater generalization ability than other networks considered in the work.

Keywords: DLFANN, FLANN, M-FLANN, MLP

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Authors: S. M. Mahbobur Rahman

Abstract:

Whole building energy simulation models are widely used for predicting future energy consumption, performance diagnosis and optimum control.  Black box building energy modeling approach has been heavily studied in the past decade. The thermal response of a building can also be modeled using a network of interconnected resistors (R) and capacitors (C) at each node called R-C network. In this study, a model building, Case 600, as described in the “Standard Method of Test for the Evaluation of Building Energy Analysis Computer Program”, ASHRAE standard 140, is studied along with a 3R2C thermal network model and the ASHRAE clear sky solar radiation model. Although building an energy model involves two important parts of building component i.e., the envelope and internal mass, the effect of building internal mass is not considered in this study. All the characteristic parameters of the building envelope are evaluated as on Case 600. Finally, monthly building energy consumption from the thermal network model is compared with a simple-box energy model within reasonable accuracy. From the results, 0.6-9.4% variation of monthly energy consumption is observed because of the south-facing windows.

Keywords: ASHRAE case study, clear sky solar radiation model, energy modeling, thermal network model.

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Authors: Ghulam Ahmad

Abstract:

In this paper comparison of Reflector Antenna analyzing techniques based on wave and ray nature of optics is presented for an offset reflector antenna using GRASP (General Reflector antenna Analysis Software Package) software. The results obtained using PO (Physical Optics), PTD (Physical theory of Diffraction), and GTD (Geometrical Theory of Diffraction) are compared. The validity of PO and GTD techniques in regions around the antenna, caustic behavior of GTD in main beam, and deviation of GTD in case of near-in sidelobes of radiation pattern are discussed. The comparison for far-out sidelobes predicted by PO, PO + PTD and GTD is described. The effect of Direct Radiations from feed which results in feed selection for the system is addressed.

Keywords: Geometrical optics & geometrical theory of diffraction, offset reflector antenna, physical optics & physical theory of diffraction, PO & GO comaprison.

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Authors: Hajir Karimi, Fakheri Yousefi, Mahmood Reza Rahimi

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An accurate and proficient artificial neural network (ANN) based genetic algorithm (GA) is developed for predicting of nanofluids viscosity. A genetic algorithm (GA) is used to optimize the neural network parameters for minimizing the error between the predictive viscosity and the experimental one. The experimental viscosity in two nanofluids Al2O3-H2O and CuO-H2O from 278.15 to 343.15 K and volume fraction up to 15% were used from literature. The result of this study reveals that GA-NN model is outperform to the conventional neural nets in predicting the viscosity of nanofluids with mean absolute relative error of 1.22% and 1.77% for Al2O3-H2O and CuO-H2O, respectively. Furthermore, the results of this work have also been compared with others models. The findings of this work demonstrate that the GA-NN model is an effective method for prediction viscosity of nanofluids and have better accuracy and simplicity compared with the others models.

Keywords: genetic algorithm, nanofluids, neural network, viscosity

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Authors: Pingping Lin, Xudong Luo, Yifan Fan

Abstract:

Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: Natural language processing, sentiment analysis, document analysis, multimodal sentiment analysis, deep learning.

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Authors: Martin Ringdorfer, Martin Horn

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A robust wheel slip controller for electric vehicles is introduced. The proposed wheel slip controller exploits the dynamics of electric traction drives and conventional hydraulic brakes for achieving maximum energy efficiency and driving safety. Due to the control of single wheel traction motors in combination with a hydraulic braking system, it can be shown, that energy recuperation and vehicle stability control can be realized simultaneously. The derivation of a sliding mode wheel slip controller accessing two drivetrain actuators is outlined and a comparison to a conventionally braked vehicle is shown by means of simulation.

Keywords: Wheel slip control, sliding mode control, vehicle dynamics.

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Authors: Davide Pierattoni, Ivan Macor, Pier Luca Montessoro

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In the recent years multimedia traffic and in particular VoIP services are growing dramatically. We present a new algorithm to control the resource utilization and to optimize the voice codec selection during SIP call setup on behalf of the traffic condition estimated on the network path. The most suitable methodologies and the tools that perform realtime evaluation of the available bandwidth on a network path have been integrated with our proposed algorithm: this selects the best codec for a VoIP call in function of the instantaneous available bandwidth on the path. The algorithm does not require any explicit feedback from the network, and this makes it easily deployable over the Internet. We have also performed intensive tests on real network scenarios with a software prototype, verifying the algorithm efficiency with different network topologies and traffic patterns between two SIP PBXs. The promising results obtained during the experimental validation of the algorithm are now the basis for the extension towards a larger set of multimedia services and the integration of our methodology with existing PBX appliances.

Keywords: Integrated voice-data communication, computernetwork performance, resource optimization.

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Authors: H.Mohammadi Majd, M.Jalali Azizpour, A.V. Hoseini

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In this paper back-propagation artificial neural network (BPANN) is employed to predict the limiting drawing ratio (LDR) of the deep drawing process. To prepare a training set for BPANN, some finite element simulations were carried out. die and punch radius, die arc radius, friction coefficient, thickness, yield strength of sheet and strain hardening exponent were used as the input data and the LDR as the specified output used in the training of neural network. As a result of the specified parameters, the program will be able to estimate the LDR for any new given condition. Comparing FEM and BPANN results, an acceptable correlation was found.

Keywords: Back-propagation artificial neural network(BPANN), deep drawing, prediction, limiting drawing ratio (LDR).

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Authors: András Barta, István Vajk

Abstract:

In pattern recognition applications the low level segmentation and the high level object recognition are generally considered as two separate steps. The paper presents a method that bridges the gap between the low and the high level object recognition. It is based on a Bayesian network representation and network propagation algorithm. At the low level it uses hierarchical structure of quadratic spline wavelet image bases. The method is demonstrated for a simple circuit diagram component identification problem.

Keywords: Object recognition, Bayesian network, Wavelets, Document processing.

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Authors: Youssou Faye, Ibrahima Niang, Thomas Noel

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Access control is a critical security service in Wire- less Sensor Networks (WSNs). To prevent malicious nodes from joining the sensor network, access control is required. On one hand, WSN must be able to authorize and grant users the right to access to the network. On the other hand, WSN must organize data collected by sensors in such a way that an unauthorized entity (the adversary) cannot make arbitrary queries. This restricts the network access only to eligible users and sensor nodes, while queries from outsiders will not be answered or forwarded by nodes. In this paper we presentee different access control schemes so as to ?nd out their objectives, provision, communication complexity, limits, etc. Using the node density parameter, we also provide a comparison of these proposed access control algorithms based on the network topology which can be flat or hierarchical.

Keywords: Access Control, Authentication, Key Management, Wireless Sensor Networks.

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Authors: Wajdi Bellil, Chokri Ben Amar, Adel M. Alimi

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This paper proposes a comparison between wavelet neural networks (WNN), RBF neural network and polynomial approximation in term of 1-D and 2-D functions approximation. We present a novel wavelet neural network, based on Beta wavelets, for 1-D and 2-D functions approximation. Our purpose is to approximate an unknown function f: Rn - R from scattered samples (xi; y = f(xi)) i=1....n, where first, we have little a priori knowledge on the unknown function f: it lives in some infinite dimensional smooth function space and second the function approximation process is performed iteratively: each new measure on the function (xi; f(xi)) is used to compute a new estimate f as an approximation of the function f. Simulation results are demonstrated to validate the generalization ability and efficiency of the proposed Beta wavelet network.

Keywords: Beta wavelets networks, RBF neural network, training algorithms, MSE, 1-D, 2D function approximation.

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Authors: Gurinder Singh Brar, Sarbjeet Singh, Harry Garg

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Wire Electric Discharge Machining (WEDM) is thermal machining process capable of machining very hard electrically conductive material irrespective of their hardness. WEDM is being widely used to machine micro scale parts with the high dimensional accuracy and surface finish. The objective of this paper is to optimize the process parameters of wire EDM to fabricate the micro channels and to calculate the surface finish and material removal rate of micro channels fabricated using wire EDM. The material used is aluminum 6061 alloy. The experiments were performed using CNC wire cut electric discharge machine. The effect of various parameters of WEDM like pulse on time (TON) with the levels (100, 150, 200), pulse off time (TOFF) with the levels (25, 35, 45) and current (IP) with the levels (105, 110, 115) were investigated to study the effect on output parameter i.e. Surface Roughness and Material Removal Rate (MRR). Each experiment was conducted under different conditions of pulse on time, pulse off time and peak current. For material removal rate, TON and Ip were the most significant process parameter. MRR increases with the increase in TON and Ip and decreases with the increase in TOFF. For surface roughness, TON and Ip have the maximum effect and TOFF was found out to be less effective.

Keywords: Micro Channels, Wire Electric Discharge Machining (WEDM), Metal Removal Rate (MRR), Surface Finish.

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Authors: C. Ardil

Abstract:

Fighter aircraft selection is one of the most critical strategies for defense multiple criteria decision-making analysis to increase the decisive power of air defense and its superior power in the defense strategy. Vague set theory is an adequate approach for modeling vagueness, uncertainty, and imprecision in decision-making problems. This study integrates vague set theory and the technique for order of preference by similarity to ideal solution (TOPSIS) to support fighter aircraft selection. The proposed method is applied in the selection of fighter aircraft for the Air Force. In the proposed approach, the ratings of alternatives and the importance weights of criteria for fighter aircraft selection are represented by the vague set theory. Finally, an illustrative example for fighter aircraft selection is given to demonstrate the applicability and effectiveness of the proposed approach. The fighter aircraft candidates were selected under six criteria including costability, payloadability, maneuverability, speedability, stealthility, and survivability. Analysis results show that the best fighter aircraft is selected with the highest closeness coefficient value. The proposed method can also be applied to solve other multiple criteria decision analysis problems. 

Keywords: fighter aircraft selection, vague set theory, fuzzy set theory, neutrosophic set theory, multiple criteria decision making analysis, MCDMA, TOPSIS

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Authors: Sachin Man Bajimaya, SangChul Park, Gi-Nam Wang

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The objective of this paper is to estimate realistic principal extrusion process parameters by means of artificial neural network. Conventionally, finite element analysis is used to derive process parameters. However, the finite element analysis of the extrusion model does not consider the manufacturing process constraints in its modeling. Therefore, the process parameters obtained through such an analysis remains highly theoretical. Alternatively, process development in industrial extrusion is to a great extent based on trial and error and often involves full-size experiments, which are both expensive and time-consuming. The artificial neural network-based estimation of the extrusion process parameters prior to plant execution helps to make the actual extrusion operation more efficient because more realistic parameters may be obtained. And so, it bridges the gap between simulation and real manufacturing execution system. In this work, a suitable neural network is designed which is trained using an appropriate learning algorithm. The network so trained is used to predict the manufacturing process parameters.

Keywords: Artificial Neural Network (ANN), Indirect Extrusion, Finite Element Analysis, MES.

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Authors: Raniya Alsharif

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The color theory of good and evil is the association of colors to the omnipresent concept of good and evil, where human behavior and perception can be highly influenced by seeing black and white, making these connotations almost dangerously distinctive where they can be very hard to distinguish. This theory is a human construct that dates back to ancient Egypt and has been used since then in almost all forms of communication and expression, such as art, fashion, literature, and religious manuscripts, helping the implantation of preconceived ideas that influence behavior and society. This is a mixed-methods research that uses both surveys to collect quantitative data related to the theory and a vignette to collect qualitative data by using a scenario where participants aged between 18-25 will style two characters of good and bad characteristics with color contrasting clothes, both yielding results about the nature of the preconceived perceptions associated with ‘black and white’ and ‘good and evil’, illustrating the important role of media and communications in human behavior and subconscious, and also uncover how far this theory goes in the age of social media enlightenment.

Keywords: Color perception, interpretivism, thematic analysis, vignettes.

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Authors: J. Jenefa, E. A. Mary Anita

Abstract:

Vehicular Ad hoc NETwork (VANET) is a kind of Mobile Ad hoc NETwork (MANET). It allows the vehicles to communicate with one another as well as with nearby Road Side Units (RSU) and Regional Trusted Authorities (RTA). Vehicles communicate through On-Board Units (OBU) in which privacy has to be assured which will avoid the misuse of private data. A secure authentication framework for VANETs is proposed in which Public Key Cryptography (PKC) based adaptive pseudonym scheme is used to generate self-generated pseudonyms. Self-generated pseudonyms are used instead of real IDs for privacy preservation and non-repudiation. The ID-Based Signature (IBS) and ID-Based Online/Offline Signature (IBOOS) schemes are used for authentication. IBS is used to authenticate between vehicle and RSU whereas IBOOS provides authentication among vehicles. Security attacks like impersonation attack in the network are resolved and the attacking nodes are rejected from the network, thereby ensuring secure communication among the vehicles in the network. Simulation results shows that the proposed system provides better authentication in VANET environment.

Keywords: Non-repudiation, privacy preservation, public key cryptography, self- generated pseudonym.

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Authors: Ahmed Abou Elfarag, A. Baith M., Hassan H. Alkhishali

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With the turn of this century, many researchers started showing interest in Embedded Firewall (EF) implementations. These are not the usual firewalls that are used as checkpoints at network gateways. They are, rather, applied near those hosts that need protection. Hence by using them, individual or grouped network components can be protected from the inside as well as from external attacks. This paper presents a study of EF-s, looking at their architecture and problems. A comparative study assesses how practical each kind is. It particularly focuses on the architecture, weak points, and portability of each kind. A look at their use by different categories of users is also presented.

Keywords: Embedded Firewall (EF), Network Interface Card (NIC), Virtual Machine Software (VMware), Virtual Firewall (VF).

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