Detection of interictal epileptic discharges (IED) events in the EEG recordings is a critical indicator for detecting and diagnosing epileptic seizures. We propose a key technology to extract the most important features related to epileptic seizures and identifies the IED events based on the interaction between frequencies of EEG with the help of a two-level recurrent neural network. The proposed classification network is trained and validated using the largest publicly available EEG dataset from Temple University Hospital. Experimental results clarified that the interaction between β and β bands, β and γ bands, γ and γ bands, δ and δ bands, θ and α bands, and θ and β bands have a significant effect on detecting the IED discharges. Moreover, the obtained results showed that the proposed technique detects 95.36% of the IED epileptic events with a false-alarm rate of 4.52% and a precision of 87.33% by using only 25 significant features. Furthermore, the proposed system requires only 164 ms for detecting a 1-s IED event which makes it suitable for real-time applications.

Recently, mobile wireless sensor network has drawn attention widely. In this paper, Joint Nodes and Sink Mobility based Immune routing-Clustering protocol (JNSMIC) is proposed to support the mobility of the sink and the sensor nodes together. It depends on using the mobile sink for solving the hot spot problem and the Multi-Objective Immune Algorithm (MOIA) for clustering the network and finding the visiting locations of the mobile sink. The JNSMIC protocol considers diferent objectives during the clustering process, namely the consumption energy, network coverage, link connection time (LCT), residual energy and mobility. Also, it reduces the computational time of finding cluster heads (CHs) by dividing it into two phases. In the frst phase, the candidate CHs set is formed based on residual energy, mobility factor and LCT of sensor nodes. While in the second phase, the MOIA algorithm is utilized to determine the final CHs subject to reducing the communication cost, improving the packet delivery ratio and ensuring network stability. JNSMIC performs the clustering process only if the remaining energy is below a threshold value thus the computational time and overhead control packets are reduced. In JNSMIC, the deputy CH concept is considered to perform the task of CH during CH failure. Furthermore, the proposed protocol performs a fault-tolerance process after transmitting each frame to maintain the link stability among CHs and their members which improves the throughput. Simulation results show that the JNSMIC protocol can effectively ameliorate the throughput while simultaneously giving lower energy expenditure and end-to-end delay.

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 paper presents 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.

Automated seizure detection system based on electroencephalograms (EEG) is an interdisciplinary research problem between computer science and neuroscience. Epileptic seizure affects 1% of the worldwide population and can lead to severe long-term harm to safety and life quality. The automation of seizure detection can greatly improve the treatment of patients. In this work, we propose a neural network model to extract features from EEG signals with a method of arranging the dimension of feature extraction inspired by the traditional method of neurologists. A postprocessor is used to improve the output of the classifier. The result of our seizure detection system on the TUSZ dataset reaches a false alarm rate of 12 per 24 hours with a sensitivity of 59%, which approaches the performance of average human detector based on qEEG tools.

The reconstruction aspect is the main core of the compressive sensing theory, in which the sparse signal is reconstructed from an incomplete set of random measurements. The constraint of sparse signal reconstruction is the minimization of l₀-norm, especially under noise condition. Thus, this paper proposes a new method called Gradient Immune-based Sparse Signal Reconstruction Algorithm for Compressive Sensing (GISSRA-CS) to optimize the trade-off between the reconstruction error and the sparsity requirements. The principle of the GISSRA-CS method is embedding the Gradient Local Search (GLS) method in the evolutionary process of the Immune Algorithm (IA) for solving the sparsity problem. Here, the sparsity problem is formulated as a multi-objective problem (MOP) by combining l₀- and l₁-norms of a solution and l₂-norm of a residual error in the same criterion to optimize the trade-off between the sparsity requirements and the error. This MOP problem is solved in a several subproblems manner by assigning different weights for each subproblem to increase the population diversity. For a long-term sparse signal, the window method is used to divide it into multiple short signals to improve the performance and computational complexity of the proposed method. Mathematical analysis and simulation experiments are presented to validate the performance and complexity of the GISSRA-CS method. Results of different simulation scenarios based on the benchmark and simulated signals show that the GISSRA-CS method outperforms the other methods in recovering the sparse signals with a small reconstruction error from noiseless and noisy measurements. Furthermore, the convergence of GISSRA-CS is faster than the other evolutionary recovery methods, but it is slower than the traditional recovery methods

Mobile Wireless Sensor Networks (MWSN) are the overgrowth and emerging technology. Routing process in MWSN is more complicated than static one. Therefore, many routing protocols have been implemented recently for MWSN to accomplish progress in energy consumption field. This paper presents a Mobility based Genetic Algorithm Hierarchical routing Protocol (MGAHP) to achieve maximum lifetime of the network and improve the stable period of MWSN. The basic idea of the proposed MGAHP protocol is using Genetic Algorithm (GA) to find the optimum number of Cluster Heads (CHs) and their locations depending on minimizing the energy consumption of the sensor nodes. Simulation results exhibited that the proposed MGAHP protocol gives better improvement in energy efficient than LEACH-M, CBR-Mobile, and MACRO protocols.

The recent energy crisis is encouraging researches to utilize renewable energies in new cities and to activate their applications. One of the most important energies is generating electricity from solar energy directly via Photovoltaics (PV), PV systems have varied and widened in many desert areas in Egypt in the current century. The current electricity supply has many defects and disadvantages, and this leads necessarily to another alternative such as PV systems, as PV efficiency has increased and their costs have been reduced, also many new visions of applying PV systems on building envelope have emerged, and many simulation software are currently used to study and evaluate the quantitative performance and the economic impact of such systems. The paper aims to evaluate applying current PV systems on the existing models of residential buildings in new cities in Egypt, and accurately the study is focusing on the youth housing models in new Assiut city, furthermore, a simulation software is used for more accuracy to calculate the quantitative adequacy and the economic impact of PV application. The paper starts with the research motivations and the need to PV systems in Egypt, and a theoretical background of current PV modules that can be used in such case studies. Then, the PV application cases on each model are specified from varying application methods, PV modules distribution, reinforcing techniques and others. Furthermore, the quantitative and economic impact is illustrated for each case by using a simulation tool. Then, analyzing characteristics, advantages and defects of each case is articled to extract the main results. Finally, the paper ends with specifying the appropriateness of applying PV systems on the case study, and the main features of extending such systems on new cities in Egypt and treating the disadvantages was discussed.

Building integrated photovoltaics (BIPV) receives growing attentions due to both architectural and engineering favorability. Large commercial building envelopes present a great potential of utilizing solar radiation, especially in climate zones with rich solar resources. Most current studies have been focused on predicting and optimizing power generation of BIPV on designed envelope systems, which leaves limited room for performance improvement of BIPV. This study introduces a framework of an optimization method that formulates the best building envelope shapes and the most matching BIPV systems. A set of criteria are established to determine the best alternatives of envelope variations, upon which the power generation and economic impact of different BIPV systems are evaluated and compared. The proposed optimization process was demonstrated using a general commercial building design application in Egypt. The developed tool can help designers in achieving an optimized building envelope that is most suitable for PV integration.

A growing attention has been paid to building integrated photovoltaics (BIPV) from both architectural and engineering favorability. There are various computational tools developed to provide computations to optimize BIPVs and often simulations for predicting their performance. This provides a great potential for designers to have different helpful tools to be utilized. This paper introduces a comparative analysis of the most common computational tools that compute or simulate main parameters of BIPVs: building energy consumption, solar exposure radiation flux and PV system performance. These computational tools are classified based the method of processing the inputs and compared using evaluation criteria. Also, optimization algorithms that can be used in optimizing BIPVs have been compared. This comparative analysis helps designers to determine better tool/s and algorithm/s for their design cases and required optimization for BIPV. The main findings of this study are the capabilities, limitations, advantages and disadvantages of each computational tool and optimization algorithm presented, in addition to the best selections among them via a comparative analysis to be used for different design cases.

A growing attention has been paid to building integrated photovoltaics (BIPV) when designing net-zero energy buildings. Envelope features of large commercial buildings can be properly designed to both enhance PV integration and reduce building energy use. Many studies have been focused on predicting PV performance of designed systems or optimizing building envelope properties to reduce energy consumption. This study introduces an optimization framework using genetic algorithm (GA) via the GenOpt program to determine the best options for building envelope designs to reduce net building energy cost and increase PV utilization capacity/efficiency. A set of envelope design features were tested in this study, such as, building dimensions, window-to-wall-ratio (WWR), orientation, and PV integration placement, upon which the associated PV and building energy cost are evaluated and compared. Cubic commercial buildings commonly found in Egypt were used to demonstrate the application of the proposed optimization process. The developed tool can help designers to determine the optimal envelopes with appropriate BIPV options from both energy and economic perspectives.