Menoufia University, Faculty of Electronic EngineeringMenoufia Journal of Electronic Engineering Research1687-118930120210101Efficient Clustering based Genetic Algorithm in Mobile Wireless Sensor Networks11214606910.21608/mjeer.2021.146069ENAsmaaRadydept. of electrical and electronics eng. faculty of electronic eng., menoufia univ.Meouf, EgyptMonaShokairdept. of electrical and electronics eng. faculty of electronic eng., menoufia, univ. Meouf, EgyptS.El-Rabaiedept. of electrical and electronics eng. faculty of electronic eng., menoufia, univ. Meouf, EgyptNabilSabordept. of electrical and electronics eng. faculty of engineering, assiut univ. Assiut, EgyptJournal Article20210203—Mobile Wireless Sensor Networks (MWSNs) has significant applications that provide free moving for sensor nodes and flexible communication with each other. MWSNs perform many improvements in energy consumption, network lifetime, and channel capacity than static WSNs. The MWSNs need more sophisticated routing protocols than static WSNs due to the unfixed topology based on nodes mobility. This paperpresents an Improved Mobility based Genetic Algorithm Hierarchical routing Protocol (IMGAHP) to handle the packet delivery ratio problem in MGAHP and maximize the network stability period. The proposed protocol is based on two main points. Firstly, utilizing the optimization process (Genetic Algorithm (GA)) to detect the optimum location of Cluster Heads (CHs) and their numbers. Secondly, reassigning timeslots allocated for sensor nodes which moved out of the cluster or didn’t have data to send, to nodes registered in secondary Time Division Multiple Access (TDMA) schedule or new joined mobile nodes. Several experiments are implemented on the proposed IMGAHP protocol using the Matlab simulation program to appraise and compare it with MGAHP and other previous protocols. It is shown from the results that the proposed IMGAHP gives preferable enhancement in packet delivery ratio, energy efficiency, and network lifetime than all previous protocols.Menoufia University, Faculty of Electronic EngineeringMenoufia Journal of Electronic Engineering Research1687-118930120210101Dynamic Modeling of Reactor Protection System in Nuclear Power Plant for Reliability Evaluation Based on State Transition Diagram132114607310.21608/mjeer.2021.146073ENMarwa A.ShoumanComputer Science and Engineering Faculty of Electronic Engineering Menoufia University Cairo, EgyptAmany S.SaberReactor Dept. Nuclear Research CenterEgyptian Atomic Energy AuthorityCairo, EgyptMohamed K.ShaatReactor Dept. Nuclear Research Center Egyptian Atomic Energy AuthorityCairo, EgyptAymanEl-SayedComputer Science and Engineering Faculty of Electronic Engineering Menoufia University Cairo, Egypt0000-0002-4437-259XHanaaTorkeyComputer Science and Engineering Faculty of Electronic Engineering Menoufia University Cairo, EgyptJournal Article20210203Reliability assessment of a digital dynamic system using traditional Fault Tree Analysis (FTA) is difficult. This paper addresses the dynamic modeling of safety-critical complex systems such as the digital Reactor Protection System (RPS) in Nuclear Power Plants (NPPs). The digital RPS is a safety system utilized in the NPPs for safe operation and shut-down of the reactor in emergency events. A quantitative evaluation reliability analysis for the digital RPS with 2-out-of-4 architecture using the state transition diagram is presented in this paper. The study assesses the effects of independent hardware failures, Common Cause Failures (CCFs), and software failures on the failure of the RPS through calculating Probability of Failure on Demand (PFD). The results prove the validity of the proposed method in analyzing and evaluating reliability of the digital RPS and also show that the CCFs and longer detection time are the main contributions to the PFD of digital RPS.Menoufia University, Faculty of Electronic EngineeringMenoufia Journal of Electronic Engineering Research1687-118930120210101An Efficient Segmentation Technique for Different Medical Image Modalities222814607710.21608/mjeer.2021.146077ENAmira A.MahmoudDepart. of Electronics and Electrical Comm., Faculty of Electronic Eng., Menoufia Uni., Egypt.WalidEl-ShafaiDepart. of Electronics and Electrical Comm., Faculty of Electronic Eng., Menoufia Uni., Egypt.0000-0001-7509-2120Taha E.TahaDepart. of Electronics and Electrical Comm., Faculty of Electronic Eng., Menoufia Uni., Egypt.El-SayedEl-RabaieDepart. of Electronics and Electrical Comm., Faculty of Electronic Eng., Menoufia Uni., Egypt.OsamaZahranDepart. of Electronics and Electrical Comm., Faculty of Electronic Eng., Menoufia Uni., Egypt.AdelEl-FishawyDepart. of Electronics and Electrical Comm., Faculty of Electronic Eng., Menoufia Uni., Egypt.Fathi E.Abd El-SamieDepart. of Electronics and Electrical Comm., Faculty of Electronic Eng., Menoufia Uni., Egypt.Journal Article20210203In this paper, a study of the segmentation of medical images is presented. The paper provides a solid introduction to image enhancement along with image segmentation fundamentals. Firstly, the local spatial information of the image is enhanced with morphological operations to ensure noise-immunity and image detail-protection. The objective of using morphological operations is to remove the defects in the texture of the image. Secondly, fuzzy c-means (FCM) clustering is used with modification of membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster centers. The proposed technique is very simple to implement and significantly fast, since it is not necessary to compute the distance between the neighboring pixels and the cluster centers. It is also efficient when dealing with noisy images because of its ability to improve membership partition matrix efficiently. Experimental results performed on different medical image modalities illustrate that the proposed technique can achieve good results, as well as short time and efficient image segmentation.Menoufia University, Faculty of Electronic EngineeringMenoufia Journal of Electronic Engineering Research1687-118930120210101Effect of Pr6O11 Substitution on Structural and Dielectric Properties of BaZrTiO5 Ceramic Materials293414608510.21608/mjeer.2021.146085ENOsama A.DesoukyBilbis Higher Institute of Engineering (BHIE), Bilbis, Sharqia , EgyptK. E.RadyEngineering Basic Sciences Department, Faculty of Engineering, Menoufia University, Shebin El-Kom, EgyptJournal Article20210203n the present work, we studied the effect of the substitution of BaO by Pr6O11, on the structure, dielectric and electrical properties of BaTiZrO5 ceramics. Samples of general formula (99.2-x) Bao-xPr6O11-0.5TiO2-0.3ZrO2 (x =0.1, 0.2, 0.5 and 0.6) named P1, P2, P3 and P4 respectively were prepared by conventional ceramic method. The structure of the prepared samples was studied using X-ray diffraction, water absorption % and SEM. Addition of Pr6O11 minimized the presence of closed pores and thus led to improved densification. The crystallize size of the prepared samples was calculated and found in the range 22-26 nm. The effect of the substitution by Pr6O11 on breakdown field, dielectric constant, ac resistivity was investigated. Finally, it was found that, the substitution BaO by Pr6O11 improves the physical properties of BaTiZrO5 ceramics by increasing their break down field, ac resistivity and dielectric constant that makes these ceramics useful in the technological applications.Menoufia University, Faculty of Electronic EngineeringMenoufia Journal of Electronic Engineering Research1687-118930120210101Performance enhancement of cooperative networks utilizing MAP decoder and Alamouti code354014608710.21608/mjeer.2021.146087ENSafwat M.Ramzydept. Electrical Engineeing, Faculty of Engineering Sohag University
Sohag, EgyptJournal Article20210203In this paper, we introduced another methodology for the cooperative network using the maximum posterior (MAP) and the Alamouti code decoding scheme for the multiple-input single-output (MISO) wireless networks that use decode and forward (DF) protocol as a cooperation protocol. Without loss of generality, the considered network consists of one source, one relay and one receiver. A closed-form expression for the upper bound of the bit error probability is derived. The obtained upper bound expression can be utilized in the power optimization problems, relay positioning issue. The results that are shown in this paper clear that the proposed scheme has two advantages over the related work. The first advantage is that it has less complexity. The second one is that it has better spectrum efficiency by using less number of channels. Therefore, our contribution can be summarized in improving the spectrum efficiency and reducing the complexity of the cooperative network, but the paid price is that the bit error rate increases by a little ratioMenoufia University, Faculty of Electronic EngineeringMenoufia Journal of Electronic Engineering Research1687-118930120210101DNA Sequences Classification with Deep Learning: A Survey415114609010.21608/mjeer.2021.146090ENSamia M.Abd –AlhalemDepartment of Electronics and
Electrical Communications
Engineering, Faculty of Electronic
Engineering, Menoufia University,
MenoufEl-Sayed M.El-RabaieDepartment of Electronics and
Electrical Communications
Engineering, Faculty of Electronic
Engineering, Menoufia University,
MenoufNaglaa. F.SolimanFaculty of Computer and
Information Sciences, Princess
Nourah Bint Abdulrahman
University, Riyadh, Saudi ArabiaSalah Eldin S. E.AbdulrahmanDepartment of Computer Science
and Engineering, Faculty of
Electronic Engineering, Menoufia
University, MenoufNabil A.IsmailDepartment of Computer Science
and Engineering, Faculty of
Electronic Engineering, Menoufia
University, MenoufFathi E.Abd El-samieDepartment of Electronics and
Electrical Communications
Engineering, Faculty of Electronic
Engineering, Menoufia University,
MenoufJournal Article20210203Deep learning (DL) methods have been<br />achieving amazing results in solving a variety of<br />problems in many different fields especially in the area<br />of big data. With the advances of the big data era in<br />bioinformatics, applying DL techniques, the DNA<br />sequences can be classified with accurate and scalable<br />prediction. The strength of DL methods come from the<br />development of software and hardware, such as<br />processing abilities graphical processing units (GPU) for<br />the hardware and new learning or inference algorithms<br />for the software, which reducing the main primary<br />difficulties that faced the training process. In This work,<br />we start from the previous classification methods such as<br />alignment methods pointing out the problems, which are<br />face to use these methods.After that, we demonstrate<br />deep learning, from artificial neural networks to hyper<br />parameter tuning, and the most recent state-of-the-art<br />DL architectures used in DNA classification. After that,<br />the paper ended with limitations and suggestions.Menoufia University, Faculty of Electronic EngineeringMenoufia Journal of Electronic Engineering Research1687-118930120210101Evaluation of Deep Learning YOLOv3 Algorithm for Object Detection and Classification525714623710.21608/mjeer.2021.146237ENWeal A.EzatMohamed M.DessoukyDept. of Computer Science and Engineering, Faculty of Electronic Engineering Menoufia University.Nabil A.IsmailDept. of Computer Science and Engineering, Faculty of Electronic EngineeringMenoufia University emailJournal Article20210204You Only Look Once version 3 (YOLOv3) is a deep learning model for object detection and classification. It is a single neural network architecture model that uses features from the feeding images and predicts bounding box for all classes of image simultaneously. This paper descript an experimental work for train the deep learning model based on YOLOv3 architecture implemented using Tensor Flow as a deep learning framework. The training process had been done using the data-set PASCAL VOC 2007 and data-set PASCAL VOC 2012 and using The Adaptive Moment Estimation Optimizer (ADM optimizer). The trained model is then tested by using the VOC 2007 test data-set. The final results evaluate the YOLOv3 deep learning model performance for object detection and classification.Menoufia University, Faculty of Electronic EngineeringMenoufia Journal of Electronic Engineering Research1687-118930120210101Enhancement Technique of Infrared Images586414624610.21608/mjeer.2021.146246ENNevenSadicDept. of Electronics and electrical communication, Faculty of electronic Engineering, Menoufia University, Egypt.EmadHassanDept. of Electronics and electrical communication, Faculty of electronic Engineering, Menoufia University, Egypt.S.El-RabaieDept. of Electronics and electrical communication, Faculty of electronic Engineering, Menoufia University, Egypt.SamiEl-dolilDept. of Electronics and electrical communication, Faculty of electronic Engineering, Menoufia University, Egypt.Moawad I.DessokyDept. of Electronics and electrical communication, Faculty of electronic Engineering, Menoufia University, Egypt.FathiAbdEl-samieDept. of Electronics and electrical communication, Faculty of electronic Engineering, Menoufia University, Egypt.Journal Article20210204This paper presents an efficient technique for enhancement of IR images, its basic idea is to use nonlinearities applied on the main trend in IR images extracted through a LPF and the details extracted through a HPF. Magnification of the details is applied, while damping of the main trend or local mean is also accomplished to attenuate the darkness effect in IRimages. Anon-linear model is applied anther trend and gain factor is applied to details .this gain is also derived from the local mean of the image the proposed approach succeeded in quality enhancement of IR imagesMenoufia University, Faculty of Electronic EngineeringMenoufia Journal of Electronic Engineering Research1687-118930120210101Machine Learning Model for Cancer Diagnosis based on RNAseq Microarray657514627710.21608/mjeer.2021.146277ENHanaaTorkeydept. computer science and engineeing Faculty of Eleronic Engineering, Menoufia University Menoufia, MenoufMostafaAtlamdept. computer science and engineeing Faculty of Eleronic Engineering, Menoufia University Menoufia, MenoufNawalEl-Fishawydept. computer science and engineeing Faculty of Eleronic Engineering, Menoufia University Menoufia, MenoufHanaaSalemCommunications and Computers Engineering Department, Faculty of Engineering, Delta University for Science and Tecnology, Gamasa, Egypt.Journal Article20210204Microarray technology is one of the most important recent breakthroughs in experimental molecular biology. This novel technology for thousands of genes concurrently allows the supervising of expression levels in cells and has been increasingly used in cancer research to understand more of the molecular variations among tumors so that a more reliable classification becomes attainable. Machine learning techniques are loosely used to create substantial and precise classification models. In this paper, a function called Feature Reduction Classification Optimization (FeRCO) is proposed. FeRCO function uses machine learning techniques applied upon RNAseq microarray data for predicting whether the patient is diseased or not. The main purpose of FeRCO function is to define the minimum number of features using the most fitting reduction technique along with classification technique that give the highest classification accuracy. These techniques include Support Vector Machine (SVM) both linear and kernel, Decision Trees (DT), Random Forest (RF), K-Nearest Neighbours (KNN) and Naïve Bayes (NB). Principle Component Analysis (PCA) both linear and kernel, Linear Discriminant Analysis (LDA) and Factor Analysis (FA) along with different machine learning techniques were used to find a lower-dimensional subspace with better discriminatory features for better classification. The major outcomes of this research can be considered as a roadmap for interesting researchers in this field to be able to choose the most suitable machine learning algorithm whatever classification or reduction. The results show that FA and LPCA are the best reduction techniques to be used with the three datasets providing an accuracy up to 100% with TCGA and simulation datasets and accuracy up to 97.86% with WDBC datasets. LSVM is the best classification technique to be used with Linear PCA (LPCA), FA and LDA. RF is the best classification technique to be used with Kernel PCA (KPCA).Menoufia University, Faculty of Electronic EngineeringMenoufia Journal of Electronic Engineering Research1687-118930120210101Shifted Legendre Polynomials For Solving Second Kind Fredholm Integral Equations768314627910.21608/mjeer.2021.146279ENShoukralla, E..SDepartment of Physics and EngineeringMathematics, Faculty of Electronic Engineering, Menoufia University Menouf, EgyptElgohary, .HDepartment of Physics and EngineeringMathematics, Faculty of Electronic Engineering, Menoufia University Menouf, EgyptMorgan., MDepartment of Physics and Engineering Mathematics, Faculty of Engineering and Technology, German University, Egypt Cairo, EgyptJournal Article20210204—in this paper, present a computational method for solving Fredholm integral equations of the second kind. The method based on the application of the shifted Legendre polynomials in matrix forms. We create a technique for extracting the Legendre coefficients of each polynomial away so that each Legendre polynomial is rewritten in the form of its coefficient’s matrix multiplied by the monomial basis function matrix. This technique significantly reduces the round off errors. By using this technique, the unknown and the data functions are expressed in two forms; each consists of three matrices. The kernel is approximated twice relevant to its two variables so that it is transformed into a form consists of a product of five matrices. By substituting by the approximate unknown function into the left and the right sides of the integral equation, we obtain an algebraic linear system of the equations without applying the collocation points. Moreover, we adapted the Gauss–Quadrature rule in an adjustment form and applied it for computing the resulted integrals. The convergence in the mean of the approximate solution and the kernel are proved. Additionally, the maximum norm error is studied, and it is found equal to zero. Numerical results are obtained for five examples to clarify the simplicity, efficiency, and reliability of the method. The obtained solutions are equal or rapidly converge to the exact solutions.Menoufia University, Faculty of Electronic EngineeringMenoufia Journal of Electronic Engineering Research1687-118930120210101Intelligent Multi-homing Switching for Vehicles Connectivity849014628110.21608/mjeer.2021.146281ENAmrElsaadanyFaculty of Engineering Pharos University in Alexandria0000-0003-4680-762XJournal Article20210204The spread of the internet applications has affected the vehicle connectivity requirements where connected vehicles can communicate with other systems to exchange data outside the vehicles. The continuous connectivity of smart vehicles is becoming very important for supporting various user applications. The challenges arise from the moving nature of vehicles on the road and the ability of providing a stable network connection due to areas where the network coverage is not available. The use of predictive multi-homed switching can eliminate the effect of network coverage holes. A switching decision can be supported by the intelligence of a connectivity gateway that analyzes network connectivity metrics collected from the vehicles such as round time delay and packet loss rate. In this paper we study the use of predictive multi-homed switching and the associated connectivity gateway in order to evaluate the improvement on the service connectivity of the vehicles. The gateway provides information ahead of time to prepare the vehicle to utilize alternative connectivity methods on different areas along the path of the vehicle. The system’s reaction to the elimination of network coverage holes is assessed where we show the improvement in the continued connectivity key performance indicators.Menoufia University, Faculty of Electronic EngineeringMenoufia Journal of Electronic Engineering Research1687-118930120210101Applying Hierarchal Clusters on Deep Reinforcement Learning Controlled Traffic Network919614628410.21608/mjeer.2021.146284ENAhmedEl-MahalawyDept. of Computer Science and
Engineering
Faculty of Electectronic engineering
Minufiya UniversityAhmedShoumanDept. of Computer Science and
Engineering
Faculty of Electectronic engineering
Minufiya UniversityAymanEl-SayedDept. of Computer Science and
Engineering
Faculty of Electectronic engineering
Minufiya University0000-0002-4437-259XFadyTaherDept. of Computer Science and
Engineering
Faculty of Electectronic engineering
Minufiya UniversityJournal Article20210204Traffic congestions is a crucial problem affecting<br />cities around the globe and they are only getting worse as the<br />number of vehicles tends to increase significantly. Traffic signal<br />controllers are considered as the most important mechanism to<br />control traffic, specifically at intersections, the field of Machine<br />Learning introduces advanced techniques which can be applied<br />to provide more flexibility and adaptiveness to traffic control<br />techniques. Efficient traffic controllers can be designed using a<br />reinforcement learning (RL) approach but major problems of<br />following RL approach are, exponential growth in the state and<br />action spaces and the need for coordination. We use real traffic<br />data of 65 intersection of the city of Ottawa to build our<br />simulations and show that, clustering the network using<br />hierarchal techniques has a great potential in reducing the stateaction<br />pair significantly and enhance overall traffic<br />performance.Menoufia University, Faculty of Electronic EngineeringMenoufia Journal of Electronic Engineering Research1687-118930120210101A Smart Model for Web Phishing Detection Based on New Proposed Feature Selection Technique9710414628610.21608/mjeer.2021.146286ENMohamed A.El-RashidyComputer Science and Engineering Department Faculty of Electronic Engineering Menoufia University EgyptJournal Article20210204Web-phishing attacks are one of the most serious cybercrime. It enables hackers to access the devices of many users and spy on their personal data such as passwords and credit card details. Hackers use a lot of tricks through the internet, which make users to share data, download files or open links that attack a computer. This research proposes meta-heuristic based approach to protect the internet users from the web-phishing. It consists of three phases, the first phase uses a new proposed method for evaluating and ranking the features of URL, HTML and JavaScript code, text, images and domain name of the web page. The second phase extracts the effective subset of the ranked features that achieves the highest classification accuracy of the web-phishing. The third phase constructs the Random forest classifier training by data features of the extracted subset. The new proposed method of the feature selection achieved the highest classification accuracy compared to the correlation feature selection, information gain, principle component analysis, and Relief feature selection algorithms. The proposed methodology of the web-phishing detection was also evaluated, it obtained the highest classification accuracy at the least possible time compared to the adaptive Neuro-fuzzy inference system.Menoufia University, Faculty of Electronic EngineeringMenoufia Journal of Electronic Engineering Research1687-118930120210101An Efficient Hybrid Technique for Noise Reduction in Optical Gyroscope Signals10511714628910.21608/mjeer.2021.146289ENHesham M.AbdelZaherCommunication Department Faculty of Electronic Engineering El-Menoufia University El-Menoufia, EgyptIbrahim M.El-DokanyCommunication Department Faculty of Electronic Engineering El-Menoufia University El-Menoufia, EgyptSami A.El-DolilCommunication Department Faculty of Electronic Engineering El-Menoufia University El-Menoufia, EgyptOsama A.OrabyElectrical Engineering Department Faculty of Engineering Damiatta University Damiatta, EgyptMoawad I.DessoukyCommunication Department Faculty of Electronic Engineering El-Menoufia University El-Menoufia, EgyptAdelEl-fishawyCommunication Department Faculty of Electronic Engineering El-Menoufia University El-Menoufia, EgyptEl_Sayed M.El_RabaieCommunication Department Faculty of Electronic Engineering El-Menoufia University El-Menoufia, EgyptFathi. E.Abd-El-SamieCommunication Department Faculty of Electronic Engineering El-Menoufia University El-Menoufia, EgyptJournal Article20210204Gyroscopes are sensors that are used for motion measurement. They are generally used to measure rotation rate of moving equipment. There are different types of gyroscopes including mechanical, micro-electro-mechanical (MEMS) and optical gyroscopes. Gyroscope signal suffers from internal noise due to internal device operation and external noise of the environment. This paper presents a proposed hybrid technique that includes both Kalman filter and wavelet denoising. Results show the superiority of this proposed technique to the other filters. Arranging the filters in cascaded hybrid structure has an effect on the performance of the hybrid technique. Using Kalman filter as a first stage is better than using the wavelet as a first stage. For the comparison, two evaluation metrics are used: Signal-to-Noise Ratio (SNR) improvement and correlation coefficient.Menoufia University, Faculty of Electronic EngineeringMenoufia Journal of Electronic Engineering Research1687-118930120210101Modeling and Identification of Hidden Objects in Dynamic Systems using Digital Filters.11812414629410.21608/mjeer.2021.146294ENFkirin,M. ADept. of Industrial and Control Eng., faculty of Electronic Eng., Minufiya University. Menof, Egypt.Youssef, M.A. SNuclear Materials Authority, PO Box 530, Maadi, Cairo, EgyptEl-Deery,M. F.Section Head @ Agiba Petroleum Company. Engineer, Naser city, Cairo, Egypt.Journal Article20210204Digital filters are used for identification, prediction, and modeling of hidden objects in dynamic systems. These filters are Gaussian filter with power spectrum depth estimation, edge detection of the hidden objects as well as constructed 2-D geomagnetic modeling of hidden objects. In this paper, digital filter results are obtained by MATLAB software. Magnetometer instrument is used to collect aeromagnetic data of dynamic systems. Aeromagnetic data are collected from Aswan area in Egypt. MATLAB codes are built to insert data and process this data in user graphic interface (UGI). The estimated depth level of hidden objects in dynamic system is selected via the power spectrum which used to transform processed data in time domain to frequency domain. Then, figure out the hidden objects in shallow and deeper levels. Edge boundary is implemented to obtain hidden objects dynamic system either shallow and deep levels. Edges and clearness hidden objects dynamic systems take out by smoothing total horizontal derivative (THDR) and enhanced total horizontal derivative (ETHDR) filter. The estimation depth of hidden objects and their extension are calculated from the 2-D modeling filter. Also, the 2-D model shown the difference hidden objects dynamic systems types through there magnetic susceptibility.Menoufia University, Faculty of Electronic EngineeringMenoufia Journal of Electronic Engineering Research1687-118930120210101Electromagnetic Absorbing Materials12512914629810.21608/mjeer.2021.146298ENFatma S.SaeedFaculty of Electronic Engineering, Menoufia University, EgyptAhmed S.ElkoranyFaculty of Electronic Engineering, Menoufia University, EgyptAdel A.SaleebFaculty of Electronic Engineering, Menoufia University, EgyptElsayed E.RabaieFaculty of Electronic Engineering, Menoufia University,
Menouf,EgyptJournal Article20210204Electromagnetic absorbing materials can be classified into: conventional absorbers, metamaterial absorbers, and reconfigurable absorbers. This paper includes a short survey on these types and there applications. It also includes the design of a thin electromagnetic absorber. The absorber is based on mushroom-like electromagnetic band gap structure with square patches. A simple procedure is developed to design the absorber. The design is checked by simulation using HFSS package. The effect of changing dimensions of the structure on absorption is evaluated. The results of the parametric study were used to trim the design and get more accurate dimensions of the structure.