@article { author = {Mohamed, Abd El-Naser and Rashed, Ahmed and Zaky, M and Elsaket, Ahmed and Gaheen, Mohamed}, title = {Simulative Study of Wavelength Division Multiplexing Fiber Bragg Grating in Nuclear Reactors Monitoring}, journal = {Menoufia Journal of Electronic Engineering Research}, volume = {28}, number = {2}, pages = {1-16}, year = {2019}, publisher = {Menoufia University, Faculty of Electronic Engineering}, issn = {1687-1189}, eissn = {2682-3535}, doi = {10.21608/mjeer.2019.62729}, abstract = {The technologies of wavelength division multiplexing (WDM) have been theoretically studied and analyzed for multiplexing fiber Bragg grating (FBG) in a single optical fiber. This method allows a single fiber to carry many of identical FBGs, making this sensor more appropriate in the nuclear reactors. The analysis demonstrates that the multiplexing capacity can be incredibly enhance small data rates and high channel spacing. The interference effect among FBGs multi-reflections channels must be taken into account. This paper simulate WDM based FBG for a channel spacing of 0.1, 0.3, 0.5, 0.8, 1 nm Gaussian apodized FBGs at data rates of 2.5, 10, 40,100,160, 250 Gb/s respectively for nuclear applications. All simulations were performed in Optisystem software.}, keywords = {}, url = {https://mjeer.journals.ekb.eg/article_62729.html}, eprint = {https://mjeer.journals.ekb.eg/article_62729_d6e031ed6db57ccb8f8904830e14f1ff.pdf} } @article { author = {Emara, Heba and Elwekeil, Mohamed and Taha, taha and El-Fishawy, Adel and El-Rabaie, Sayed and Alotaiby, Turky and Alshebeili, Saleh and Abd el-samie, Fathi}, title = {Efficient Epileptic Seizure Prediction Approach Based on Hilbert Transform}, journal = {Menoufia Journal of Electronic Engineering Research}, volume = {28}, number = {2}, pages = {17-32}, year = {2019}, publisher = {Menoufia University, Faculty of Electronic Engineering}, issn = {1687-1189}, eissn = {2682-3535}, doi = {10.21608/mjeer.2019.62744}, abstract = {This paper introduces a patient-specific method for seizure prediction applied to scalp Electroencephalography (sEEG) signals. The proposed method depends on computing the instantaneous amplitude of the analytic signal by applying Hilbert transform on EEG signals. Then, the Probability Density Functions (PDFs) are estimated for amplitude, local mean, local variance, derivative and median as major features. This is followed by a threshold-based classifier which discriminates between pre-ictal and inter-ictal periods. The proposed approach utilizes an adaptive algorithm for channel selection to identify the optimum number of needed channels which is useful for real-time applications. It is applied to all patients from the CHB-MIT database, achieving an average prediction rate of 96.46%, an average false alarm rate of 0.028077/h and an average prediction time of 60.1595 minutes using a 90-minute prediction horizon. Experimental results prove that Hilbert transform is more efficient for prediction than other existing approaches.}, keywords = {}, url = {https://mjeer.journals.ekb.eg/article_62744.html}, eprint = {} } @article { author = {Shawky, Hala and Abd-Elnaby, Mohamed and Rihan, Mohamed and Nassar, Mohamed and El-Fishawy, Adel and Abd El-Samie, Fathi}, title = {Efficient Remote Access System Based on Coded Speech Signals}, journal = {Menoufia Journal of Electronic Engineering Research}, volume = {28}, number = {2}, pages = {33-52}, year = {2019}, publisher = {Menoufia University, Faculty of Electronic Engineering}, issn = {1687-1189}, eissn = {2682-3535}, doi = {10.21608/mjeer.2019.62761}, abstract = {This paper investigates an approach for speaker identification in a remote access system based on coded speech signals. The aim of using the coding process is to decrease the amount of transmitted data via the channel. In this proposed system, the speech signal is coded by two different coding techniques. It can be coded either by linear predictive coding or compressive sensing. The coded speech signal is transmitted into the receiver via the wireless communication channel. At the receiver, the received signal is decoded, and then speaker identification system is applied on the decoded signal. During the transmission process, the channel errors affect on the transmitted signal, so they should be taken into account. The speaker identification process is used to achieve the security needed for the remote access system. In speaker identification system, the feature vectors are captured from different discrete transforms such as discrete wavelet transform, discrete cosine transform, and discrete sine transform, besides the time domain. The recognition rate for all transforms is computed to evaluate the effect of coded signals on the performance of the speaker identification system. The results proved that the discrete cosine transform and discrete wavelet transform are the best. In addition to the proposed system gives close recognition results to those obtained from real speech signals revealing a simple degradation effect due to the speech coding.}, keywords = {}, url = {https://mjeer.journals.ekb.eg/article_62761.html}, eprint = {} } @article { author = {Hamad, Asmaa and Taha, Taha and El-Rabaie, Sayed and El-Fishawy, Adel and Alotaiby, Turky and Alshebeili, Saleh and Abd El-Samie, Fathi}, title = {Sub-band Decomposition for Epileptic Seizure Prediction}, journal = {Menoufia Journal of Electronic Engineering Research}, volume = {28}, number = {2}, pages = {53-64}, year = {2019}, publisher = {Menoufia University, Faculty of Electronic Engineering}, issn = {1687-1189}, eissn = {2682-3535}, doi = {10.21608/mjeer.2019.62763}, abstract = {This paper presents a frame work for the segmentation of EEG signals into three distinctive patterns; normal, pre-ictal and ictal based on sub-band decomposition. The objective of this segmentation process is to implement it on a mobile connected wirelessly to the electrode headset in order to give audio or visual alarms to epilepsy patients in case of epileptic seizure approaching. EEG signals contain five bands; Delta, Theta, Alpha, Beta, and Gamma (δ, θ α, β, and γ). The study in this paper tests each sub-band for possibility of seizure prediction. The sub-band decomposition is performed with IIR filters. The prediction method adopts a statistical approach that has training and testing phases. The training phase comprises estimation of five signals attributes; amplitude, derivative, local mean, local variance, and median. The PDF of each attribute is estimated for normal and pre-ictal states. Based on pre-set prediction probability and false alarm probability constraint a process of channel selection and bin selection from the PDFs of the selected as a tool for feature reduction and selection. The testing phase is performed with a threshold strategy on the selected bins. A majority voting strategy with a moving average smoothing filters is used for decision making. Simulation results proved the feasibility of the gamma band for seizure prediction.}, keywords = {}, url = {https://mjeer.journals.ekb.eg/article_62763.html}, eprint = {} } @article { author = {El-Soudy, Salma and El-Sayed, Ayman and Khalil, Adnan and Khalil, Irshad and Taha, Taha and Abd El-Samie, Fathi}, title = {An Efficient Method Of ECG Beats Feature Extraction/Classification With Multiclass SVM Error Correcting Output Codes}, journal = {Menoufia Journal of Electronic Engineering Research}, volume = {28}, number = {2}, pages = {65-78}, year = {2019}, publisher = {Menoufia University, Faculty of Electronic Engineering}, issn = {1687-1189}, eissn = {2682-3535}, doi = {10.21608/mjeer.2019.62765}, abstract = {This paper presents an efficient algorithm for classifying the ECG beats to the main four types. These types are normal beat (normal), Left Bundle Branch Block beats (LBBB), Right Bundle Branch Block beats (RBBB), Atrial Premature Contraction (APC). Feature extraction is performed from each type using Legendre moments as a tool for characterizing the signal beats. A Multiclass Support Vector Machine (multiclass SVM) is used for the classification on process with Legendre polynomial coefficients as inputs. A comparison study is presented between the proposed and some existing approaches. Simulation results reveal that the proposed approach gives 97.7% accuracy levels compared to 95.7447%, 95.88%, 95.03% , 93.40%, 96.02%, 95.95%, 96.24% achieved with Discrete wavelet (DWT), Haar wavelet and principle component analysis (PCA) as feature extractors and ANN, Simple Logic Random Forest, LibSVM and J48 as classifiers.}, keywords = {Legendre Polynomials,Shifted Legendre Polynomials,classification,Multiclass Support Vector Machine}, url = {https://mjeer.journals.ekb.eg/article_62765.html}, eprint = {} } @article { author = {Ellakany, Abdelhady and Abouelatta, Mohamed and Hafez, I. and El-Rabaie, S. and Gontrand, Christian}, title = {Towards 3D Nuclear Detectors}, journal = {Menoufia Journal of Electronic Engineering Research}, volume = {28}, number = {2}, pages = {79-96}, year = {2019}, publisher = {Menoufia University, Faculty of Electronic Engineering}, issn = {1687-1189}, eissn = {2682-3535}, doi = {10.21608/mjeer.2019.62767}, abstract = {The main idea of this paper is to indicate the developments of nuclear detectors and explain the advantages of using 3D structures. The 3D detectors are considered as potential candidates with wide band gap material. The 3D semiconductor detectors show advantages over the 2D traditional detectors. The applications include Γ and X-rays detection for Large Hadron Collider (LHC), Super Large Hadron Collider (SLHS) and hard radiation for high energy physics tests. In addition, the fast signal response is needed with sensitive border regions (electrodes) to overcome disadvantages of planar detectors. The unique geometry of 3D detectors with new material like cadmium telluride (CdTe) are satisfying many of these requirements and have several advantages over planar detectors. Hard radiation and short collection time response have become very important for modern SLHC and X-ray imaging for molecular biology. The 3D detectors have fast charge collection times that can arrive to 4×10-12 sec at 15 V and they are also suitable for stopping power up to 3 TeV to meet the future applications of SLHC requirements.}, keywords = {}, url = {https://mjeer.journals.ekb.eg/article_62767.html}, eprint = {} } @article { author = {El-Gindy, Sally and El-Dolil, Sami and El-Fishawy, Adel and El-Rabaie, El-Sayed and Dessouky, Moawad and Abd El-Samie, Fathi and Elotaiby, Turky and Elshebeily, Saleh}, title = {Sensitivity of Seizure Pattern Prediction to EEG Signal Compression}, journal = {Menoufia Journal of Electronic Engineering Research}, volume = {28}, number = {2}, pages = {97-116}, year = {2019}, publisher = {Menoufia University, Faculty of Electronic Engineering}, issn = {1687-1189}, eissn = {2682-3535}, doi = {10.21608/mjeer.2019.62768}, abstract = {This paper presents a framework for Electroencephalography (EEG) seizure prediction in time domain. Moreover, it studies an efficient lossy EEG signal compression technique and its effect on further processing for seizure prediction in a realistic signal acquisition and compression scenario. Compression of EEG signals are one of the most important solutions in saving speed up signals transfer, reduction of energy transmission and the required memory for storage in addition to reduction costs for storage hardware and network bandwidth. The main objective of this research is to use trigonometric compression techniques including; Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) algorithms on EEG signals and study the impact of the reconstructed EEG signals on its seizure prediction ability. Simulation results show that the DCT achieves the best prediction results compared with DST technique achieving sensitivity of 95.238% and 85.714% respectively. The proposed approach gives longer prediction times compared to traditional EEG seizure prediction approaches. Therefore, it will help specialists for the prediction of epileptic seizure as earlier as possible.}, keywords = {}, url = {https://mjeer.journals.ekb.eg/article_62768.html}, eprint = {} } @article { author = {Shalaby, Mohamed and Shokair, Mona and Messiha, Nagy}, title = {Interference Mitigation Techniques Applied to LTE Femtocells Systems}, journal = {Menoufia Journal of Electronic Engineering Research}, volume = {28}, number = {2}, pages = {117-128}, year = {2019}, publisher = {Menoufia University, Faculty of Electronic Engineering}, issn = {1687-1189}, eissn = {2682-3535}, doi = {10.21608/mjeer.2019.62770}, abstract = {The LTE Femtocells system can satisfy the users' requirements regarding the throughput in indoor zones and the outdoor ones. Thanks to the femtocells' deployment, the LTE Femtocells system can satisfy the fourth generation requirements beside to the good existent coverage. When the femtocells are deployed inside the LTE macrocells, a mutual electromagnetic interference, EMI, can exist between the macrocells and the femtocells. This interfernce eresults from the operation of the macrocells and the femtocells on the same spectrum. There should be an interference mitigation technique in order to improve the performance of the LTE femtocells system. The scope of this paper is the summarization of the different tools, which can be applied, in order to reduce or completely mitigate the interference between the macrocells and the femtocells. These tools are illustrated in details and simulated. Moreover, their performance is compared.}, keywords = {}, url = {https://mjeer.journals.ekb.eg/article_62770.html}, eprint = {} } @article { author = {Ahmed, Wael and Farahat, Ashraf}, title = {Integrated Capacitance-Ultrasonic Sensor for Gas-Liquid-Solid Multiphase Measurements: A Proof of Concept}, journal = {Menoufia Journal of Electronic Engineering Research}, volume = {28}, number = {2}, pages = {129-152}, year = {2019}, publisher = {Menoufia University, Faculty of Electronic Engineering}, issn = {1687-1189}, eissn = {2682-3535}, doi = {10.21608/mjeer.2019.62771}, abstract = {Capacitance and ultrasonic sensors are used to detect solid particles in a multiphase flow mixture. In this study, it is proposed to utilize the capacitance and ultrasonic techniques in an integrated industrial device that can be used in gas-liquid-solid multiphase flow measurements for practical purposes. The key feature of the developed integrated sensor is the ability of the ultrasound sensor to detect the concentration of the solid particles while the capacitance sensor identifies the ratio between the gas and liquid phase in the total mixture. Two-dimensional finite element analysis using COMSOL© is used to design the optimum sensor configuration and to show the feasibility of the developed sensor. Experiments were performed utilizing materials that mimic a frozen multiphase flow mixture to perform static tests to determine the calibration coefficient and validate the sensor design. The need for multiphase flow measurement in the oil and gas production and petrochemical industries has been significantly increased over the last few years. Reliable measurements of the multiphase flow parameters such as void fraction, phase concentration, phase velocity and flow pattern identification are important for accurate modelling and/or in the operation of multiphase systems. Although many multiphase flow meters were recently developed, challenges in measuring multiphase flow components remain unresolved. Therefore, extensive research efforts were spent in designing accurate multiphase flow meters and several meters are currently under development worldwide. However, due to the complexity of the multiphase flow mixture and in some cases when three or more phases co-exist, it is difficult to adopt only one technique to develop a multiphase flow meter. Consequently, the integration of multiple sensors, based on several measurement techniques, found to be the optimum solution for accurate multiphase flow metering. In this study we investigated both capacitance and ultrasonic techniques for their potential use in detecting solid particles in multiphase flow mixture.}, keywords = {Multiphase flow,capacitance sensors,Ultrasonic,gas liquid solid meter}, url = {https://mjeer.journals.ekb.eg/article_62771.html}, eprint = {} } @article { author = {Mostafa, Tamer and El-Rabaie, El-Sayed}, title = {Literature Review on All-Optical Photonic Crystal Encoders and Some Novel Trends}, journal = {Menoufia Journal of Electronic Engineering Research}, volume = {28}, number = {2}, pages = {153-184}, year = {2019}, publisher = {Menoufia University, Faculty of Electronic Engineering}, issn = {1687-1189}, eissn = {2682-3535}, doi = {10.21608/mjeer.2019.62773}, abstract = {The all-optical encoder (AOE) based on photonic crystals (Ph.Cs.) is one of the most important devices in computing systems. The essential related parameters are the delay time, the switching speed and the contrast ratio (CR). Moreover, the design simplicity, the compact size and the multi-wavelength operation have come as a fabrication and functional relevant attributes. Throughout the upcoming lines, an introduction for the important assessment factors and definitions will be presented. Finite difference time domain (FDTD) and plane wave expansion (PWE) methods were used for analyzing all structures. An intensive overview of the photonic crystals (AOE) was achieved for the recently published (4x2) and (8x3) types. The corresponding functional parameters for each design were explored, and comparison tables were organized. Finally, numerical methods were discussed with the accompanying commercial software packages; then a future view for the higher-performance operation was attained.}, keywords = {All-optical encoder,Photonic crystal,Ring resonator,Switching speed,Self- collimation,Kerr effect}, url = {https://mjeer.journals.ekb.eg/article_62773.html}, eprint = {} } @article { author = {Aboalneel, Mahmoud and Abd-Elnaby, Mohammed and El-Sayed, Ayman}, title = {Quality-based LEACH protocol with enhanced cluster-head selection for wireless sensor networks}, journal = {Menoufia Journal of Electronic Engineering Research}, volume = {28}, number = {2}, pages = {185-202}, year = {2019}, publisher = {Menoufia University, Faculty of Electronic Engineering}, issn = {1687-1189}, eissn = {2682-3535}, doi = {10.21608/mjeer.2019.62774}, abstract = {In Wireless Sensor Networks (WSNs), the main challenge is the design of an efficient routing protocol which has a major impact on the reduction of energy consumption. In this paper, the proposed routing protocol is based on Signal to Noise Ratio (SNR) to improve the performance of WSNs. In the proposed routing protocol, the residual energy is used to elect the Primary Cluster Head (P-CH), while the SNR of received signal, residual energy and the distance from a node to a sink are used to elect Secondary Cluster Head(S-CH). This proposed election mechanism helps the S-CH to use a better-quality RF channel and provides a good packet delivery ratio with minimum energy consumption. The simulation results demonstrate in static mode and Gauss-Markov Mobility Model (GMMM) as, mobile mode. The proposed routing protocol extends the network lifetime by decreasing the energy consumption and reduces overhead data. Also, it compares with C-LEACH, TL-LEACH, and A-TEEN protocols.}, keywords = {WSN,Routing protocols,Cluster head selection,SNR,energy consumption}, url = {https://mjeer.journals.ekb.eg/article_62774.html}, eprint = {} } @article { author = {Al-Azrak, Faten and Dessouky, Moawad and Abd El-Samie, Fathi and Elkorany, Ahmed and Elsharkawy, Zeinab}, title = {Image Forgery Detection Based on Trigonometric Transforms}, journal = {Menoufia Journal of Electronic Engineering Research}, volume = {28}, number = {2}, pages = {203-216}, year = {2019}, publisher = {Menoufia University, Faculty of Electronic Engineering}, issn = {1687-1189}, eissn = {2682-3535}, doi = {10.21608/mjeer.2019.62776}, abstract = {Image forgery detection is the basic key to solve many problems, especially with regard to the social problems such as those in Facebook, and court cases. Copy-move forgery is the type of forgery where a part of the image is copied to other location of the same image to hide important information or duplicate certain objects in the original image which makes the viewer suffer from difficulties to detect the forged region. In this type of image forgery, it is easy to perform forgery, but more difficult to detect it, because the features on the copied parts are similar to those of other parts of the image. This paper presents a comparison study between different trigonometric transforms in 1D and 2D for detecting the forgery parts in the image. This comparison study is based on the completeness rate and the time of processing for the detection. This comparison concludes that the DFT in 1D or 2D implementation is the best choice to detect copy-move forgery compared to other trigonometric transforms. The proposed algorithm can also be used for active forgery detection because of its robustness to detect the manipulation of digital watermarked images or images with signatures.}, keywords = {Image forgery detection,Trigonometric transforms,Copy-move forgery,Multimedia security}, url = {https://mjeer.journals.ekb.eg/article_62776.html}, eprint = {} } @article { author = {Ghozia, Ahmed and Attiya, Gamal and El-Fishawy, Nawal}, title = {The Power of Deep Learning: Current Research and Future Trends}, journal = {Menoufia Journal of Electronic Engineering Research}, volume = {28}, number = {2}, pages = {217-244}, year = {2019}, publisher = {Menoufia University, Faculty of Electronic Engineering}, issn = {1687-1189}, eissn = {2682-3535}, doi = {10.21608/mjeer.2019.62778}, abstract = {Deep learning, in general, is about multi layered neural networks copying the structure and intellectual procedure of the human mind. Rather than handcrafted features, it permits the procurement of knowledge straightforwardly from information. They relapse mind boggling target works in a nested system, where more complex forms with bigger receptive fields are estimated using less abstract ones. Deep learning additionally makes it conceivable to consider formal domain knowledge and supplant an extensive collection of traditional algorithmic methods with flexible differentiable modules. These all strengthen and empower deep learning and make it adaptable while establishing the connection between the input information and target yield. Research frontiers are presently moving toward the rest of the difficulties. This paper presents a complete overview about deep learning. It illustrates where did deep learning initiate from, what had been accomplished using deep learning, What research areas are currently being investigated via deep learning, and most importantly What are the challenges and open problems of deep learning - as those are the issues, once handled, will lead to achieve the general conscious Artificial Intelligence (AI). The purpose is to empower graduates, practitioners, researchers and fans toward a powerful cooperation in the field of deep learning.}, keywords = {Deep learning,Neural Networks,Reinforcement Learning,unsupervised learning,generative models,meta learning,Symbolic AI}, url = {https://mjeer.journals.ekb.eg/article_62778.html}, eprint = {} } @article { author = {Abdulaziz, Sally and Nabil, Essam and Zaki, Gomaa and Atlam, Galal}, title = {Tuning the Parameters of TSK Neuro-Fuzzy System by Particle Swarm Optimization}, journal = {Menoufia Journal of Electronic Engineering Research}, volume = {28}, number = {2}, pages = {245-258}, year = {2019}, publisher = {Menoufia University, Faculty of Electronic Engineering}, issn = {1687-1189}, eissn = {2682-3535}, doi = {10.21608/mjeer.2019.62781}, abstract = {Particle Swarm Optimization (PSO) algorithm is applied to improve the efficiency of Takagi-Sugeno-Kang (TSK) neuro-fuzzy network in identification of nonlinear system. First, a TSK type neuro-fuzzy system is adopted for improving identification and prediction, and then PSO technique is adopted to optimize the execution of neuro-fuzzy network. The simulation results indicate that the applied PSO accomplishes good performance and tracks the plant output with minimal error.}, keywords = {}, url = {https://mjeer.journals.ekb.eg/article_62781.html}, eprint = {} } @article { author = {Ghozia, Ahmed and Attiya, Gamal and El-Fishawy, Nawal}, title = {Towards the Conceptual Retrieval of Multimedia Documentary: A Survey}, journal = {Menoufia Journal of Electronic Engineering Research}, volume = {28}, number = {2}, pages = {259-286}, year = {2019}, publisher = {Menoufia University, Faculty of Electronic Engineering}, issn = {1687-1189}, eissn = {2682-3535}, doi = {10.21608/mjeer.2019.62785}, abstract = {Billions of active online users are continuously feeding the world with multimedia Big Data through their smart phones and PCs. These heterogenous productions are existing in different social media platforms, such as Facebook and Twitter, delivering a composite message in the form of audio, visual and textual signals. Analyzing multimedia Big Data to understand the intended delivered message, had been a challenge to audio, image, video and text processing researchers. Thanks to the recent advances in deep learning algorithms, researchers had been able to improve the performance of multimedia Big Data analytics and understanding techniques This paper presents a survey on how a multimedia file is analyzed, key challenges facing multimedia analysis, and how deep learning is helping conquer and advance beyond those challenges. Future directions of multimedia analysis are also addressed. The aim is to stay objective all through this study, bringing both empowering enhancements and in addition inescapable shortcomings, wishing to bring up fresh questions and stimulating new research frontiers for the reader.}, keywords = {Multimedia analysis,video understanding,Image classification,speech recognition,natural language processing,Deep learning}, url = {https://mjeer.journals.ekb.eg/article_62785.html}, eprint = {} } @article { author = {Ghanem, Hanan and El-Shafai, Walid and El-Rabaie, El-Sayed and Mohamed, Abd Naser and Rashed, Ahmed and Abd El-Samie, Fathi and Tabbour, Mohammed}, title = {Quality Assessment of Images Transmitted over Optical Fiber Communications Systems based on Statistical Metrics}, journal = {Menoufia Journal of Electronic Engineering Research}, volume = {28}, number = {2}, pages = {287-304}, year = {2019}, publisher = {Menoufia University, Faculty of Electronic Engineering}, issn = {1687-1189}, eissn = {2682-3535}, doi = {10.21608/mjeer.2019.62791}, abstract = {This paper presents two efficient approaches for radiation effect identification using signal and image processing concepts. The first approach depends on k-means clustering performed on spectrometer counts versus wavelength. The other approach depends on the analysis of zebra chart image based on depth/edge model. Quality metrics of the received images are used for classification on of the cases with and without radiation. Simulation results reveal high efficiency of the two proposed approaches for radiation effect detection.}, keywords = {Radiation detection,gamma radiation,K-mean clustering,Artificial Neural Networks,Image processing}, url = {https://mjeer.journals.ekb.eg/article_62791.html}, eprint = {} }