The design procedure for ledges of L-shaped beams presented in the seventh edition of the PCI Design Handbook: Precast and Prestressed Concrete has been called into question by many engineers and researchers. Research findings from previous experimental studies have indicated that the ledge design equations provided in the seventh edition of the PCI Design Handbook overestimate the ledge punching-shear capacity. This paper presents the findings of the first phase of a comprehensive experimental program conducted with the objective of developing design guidelines for the ledges of L-shaped beams. In this first phase of study, short-span beams were used to minimize the effect of global stresses and the cost of testing, thus allowing for a larger number of parameters to be examined. The main objectives of this study were to investigate the ledge behavior and the configuration of the failure surface. In addition, the study also investigated the effect of various parameters believed to affect ledge behavior. The study also investigated the performance of special reinforcement details toward the development of detailing recommendations for ledge reinforcement. Research findings indicate that even with low levels of global stress, the ledge design procedure provided in the seventh edition of the PCI Design Handbook could overestimate the ledge capacity. Furthermore, the observed failure surface was generally larger than the assumed surface specified by the PCI procedure. The study also found that several parameters affected the ledge capacity but are not considered by the PCI procedure. Finally, the study also demonstrated that certain reinforcement details can be used to improve the ledge behavior and to enhance the ledge capacity.

Droop control has been widely used as a load-sharing method between paralleled power sources in DC microgrid due to its modularity and reliability. Existing droop gains design methods rely on computationally intensive supervisory control algorithms and knowledge of sub-system parameters.This paper presents a streamlined design approach for optimal droop gains, relying only on the knowledge of the parameters of the local converter power losses model in order to achieve minimum power losses for a More Electric Aircraft (MEA) DC microgrid. Additionally, a simplified, but sufficiently accurate, converter losses model is proposed in this paper for optimal droop gains design. The proposed converter losses model consists of two parts; no-load losses, and losses which are represented by Equivalent Series Resistance (ESR). The proposed design approach analyses show that setting the optimal droop gains equal to the converter ESR will achieve minimum overall DC microgrid power losses without the need for any additional information on the DC transmission line parameters. The effectiveness of the optimal droop gains design method is tested in a simulation environment and evaluated experimentally using a laboratory DC microgrid test rig.

Routing protocols are responsible for discovering and maintaining energy-efficient routes in wireless sensor networks (WSNs) to make reliable and efficient communication. The main aim of the routing protocol design is collecting data of the sensor field efficiently. In general, routing in WSNs can be classified into three groups: flat routing, hierarchical routing, and location routing. According to the literature, hierarchical routing has more advantages compared to other types, for example, hierarchical routing reduces the redundant data transmission and balances the load among the sensor nodes in an efficient way. Recently, many intelligent-based hierarchical routing protocols are developed for controlling the consumption power of WSNs. Selecting an appropriate routing protocol for specific applications is an important and difficult task for the designer of WSNs. Therefore, this chapter presents a comprehensive survey of the recently intelligent-based hierarchical routing protocols that are developed based on Particle Swarm Optimization, Ant Colony Optimization, Fuzzy Logic, Genetic Algorithm, and Artificial Immune Algorithm. These protocols will review in detail according to different metrics such as WSN type, node deployment, control manner, network architecture, clustering attributes, protocol operation, path establishment, communication paradigm, energy model, protocol objectives, and applications. Moreover, a comparison between the reviewed protocols is investigated here depending on delay, network size, energy efficiency, and scalability with mentioning the advantages and drawbacks of each protocol.

Wireless sensor networks (WSNs) integrate sensor technology, microelectromechanical systems, and wireless network technologies. Saving energy and ensuring network connectivity are the most important challenges to extend the lifetime of WSNs, and optimal coverage and routing are the keys to it. The deployment strategy of sensor nodes is the most important factor for ensuring network coverage. In this chapter, two centralized energy-efficient deployment algorithms are proposed depending on one of the inspired computing algorithms called multi-objective immune algorithm (MOIA) to optimize the trade-off between the network coverage and the energy cost. The first deployment algorithm is called an immune-based node deployment algorithm (INDA). The INDA considers the dissipated energy in the mobility besides the network coverage during the relocation operation with considering the effect of the obstacles and field’s boundaries, while the second deployment algorithm is called a centralized Voronoi-based immune deployment algorithm (CVIDA) that mixes the MOIA and the Voronoi diagram. The CVIDA considers the dissipated energy in the mobility, the sensing, and the redundant coverage in its objective function besides the network coverage. CVIDA finds the locations of the sensor nodes and the optimal working nodes based on reducing the mobility cost, adjusting the sensing range, and controlling the communication radio of each node. Many experiments are conducted to validate the performance of the proposed algorithms compared to the state of the art.

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