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This paper proposes a new modulation-driven pipelining approach for reducing the complexity of speculative decision feedback equalizers (DFEs). The scalable receiver architecture enables the detection and equalization of multiple pulse amplitude modulation (PAM) signaling schemes using same hardware. The receiver is architected into parallel slices of pipelined layers that achieve significant energy and area savings compared to conventional realizations. The fine modularity in two dimensions of the proposed receiver circuit facilitates the clock gating of unused components to ensure energy-proportional behavior. Simulink simulations were used to discuss the parallel and multi-layer operation of the proposed receiver in both detection and equalization modes. The results from the model also show no performance degradation from the new receiver architecture when configured for PAM8/6/4/3/2 operation. Transistor level design and physical layout of a 1-tap PAM4 DFE/3-tap PAM2 DFE were used to discuss design considerations and trade-offs. Pre-layout validation results indicate that seamless reconfigurability and complexity reduction can be achieved at comparable or better area and power to architectures that constrain the receiver intersymbol interference (ISI).

This work addresses the important issue of frequency stability in shipboard microgrids (SMGs), which are confronted with difficulties because of the alternating power insertion from green sources and low system inertia. In order to sustain a consistent frequency (despite system uncertainties), a study and precisely calibrated regulator is necessary. Therefore, this article proposed a new combined three degree of freedom proportional integral derivative (3DOF-PID) and tilted-integral (TI) regulators for achieving enhanced system performance, robustness, and enhance the frequency performance of a power grid. The superior performance of the suggested 3DOF-PID-TI has been assured by comparing it with three distinct controller architectures (FOPID, PDPID2 and 2DOF-PID) for the multi-energy SMG system. The investigation also took into account the temporal delays brought on by the communication links between the sensor and the regulator. The triangulation topology aggregation optimizer (TTAO), a relatively new meta-heuristic technique that hasn’t been employed to load frequency control (LFC) issues until recently, was employed to adjust the controllers’ parameters. The TTAO’s frequency performance outcomes were thoroughly contrasted to those of other optimization algorithms (i.e., Chimp, Whale, and Gradient-Based Optimization Algorithms) so as to evaluate the optimization efficacy of the suggested optimizer. The findings showed that the proposed 3DOF-PID-TI controller, whose parameters has been tuned by TTAO, outperformed its competitors in terms of overshoot (15.87mHz), undershoot (-29.04mHz), settling time (2.49sec) and performance index (i.e., ITAE(0.0171)). Additionally, the suggested controller’s robustness was assessed in a range of SMG operating situations as ex. (Random Multi-Step Variation in Load, Real Data on Stochastic Power Fluctuations, Energy Storage System Impact on the system and sensitivity analysis). The acquired data amply proved that when crucial system parameters experience a substantial variation, the controller’s gains set under normal circumstances do not need to be re-tuned.

Subsidence, the downward vertical land motion (VLM), plays a pivotal role in contributing to the risk of coastal flooding. Accurately estimating VLM and identifying its potential features related to subsidence can provide crucial information for stakeholders to make better-informed decisions. This study aimed to estimate large-scale subsidence at the Texas Gulf Coast and identify potential subsidence features using explainable models. Nine potential features were considered for modeling the VLM, ranging from natural terrain variations to anthropogenic activities. These features were used to train a random forest (RF) machine learning model. Explainable artificial intelligence (XAI) techniques including SHapley Additive exPlanations (SHAP) and impurity- and permutation-based feature importance were used to identify the contributions to subsidence. The results demonstrated favorable performance of the RF model, achieving an $R^{2}$ value of 0.56 during validation. XAI results underscored the significance of the digital elevation model in explaining subsidence at the Texas Coast. Additionally, XAI analysis highlighted the overall contribution of subsidence from anthropogenic activities, such as hydrocarbon extraction and groundwater withdrawal. Furthermore, the sample-level SHAP map provided detailed and reasonable subsidence-attribution results across the study area, showing potential for automatic and data-driven explanations of the VLM.

Knowledge graphs (KGs) that follow the Linked Data principles are created daily. However, there are no holistic models for the Linked Open Data (LOD). Building these models(i.e., engineering a pipeline system) is still a big challenge in order to make the LOD vision comes true. In this paper, we address this challenge by presenting NELLIE, a pipeline architecture to build a chain of modules, in which each of our modules addresses one data augmentation challenge. The ultimate goal of the proposed architecture is to build a single fused knowledge graph out of the LOD. NELLIE starts by crawling the available knowledge graphs in the LOD cloud. It then finds a set of matching KG pairs. NELLIE uses a two-phase linking approach for each pair (first an ontology matching phase, then an instance matching phase). Based on the ontology and instance matching, NELLIE fuses each pair of knowledge graphs into a single knowledge graph. The resulting fused KG is then an ideal data source for knowledge-driven applications such as search engines, question answering, digital assistants and drug discovery. Our evaluation shows an improved $Hit \text{@} 1$ score of the link prediction task on the resulting fused knowledge graph by NELLIE in up to 94.44% of the cases. Our evaluation also shows a runtime improvement by several orders of magnitude when comparing our two-phases linking approach with the estimated runtime of linking using a naïve approach.

This paper proposes a hybrid phase detector for use in multilevel timing recovery systems. The proposed approach suppresses errant zero-crossings associated with multilevel baud rate phase detectors and ensures maximum signal swing in lock, with minimal hardware and power overhead. Analysis and simulation results in a 28nm CMOS process are used to explore the functionality of proposed phase detector and demonstrate its effectiveness in achieving superior performance to the conventional approach.Clock and data recovery (CDR) loop simulations show that the proposed phase detector enables $1.36\times $ increase in vertical eye margin while maintaining similar steady-state RMS jitter and compared to the conventional approach. The simulations also show effective suppression of unwanted phase detector zero-crossing, while achieving comparable acquisition bandwidth to the conventional approach.

The Federated learning (FL) technique resolves the issue of training machine learning (ML) techniques on distributed networks, including the huge volume of modern smart devices. FL clients frequently use Wi-Fi and have to interact in unstable network surroundings. However, as the present FL aggregation approaches receive and send a large number of weights, accuracy can be decreased considerably in unstable network surroundings. Therefore, this study presents a Quantum with Metaheuristics Algorithm Based Minimization of Communication Costs in Federated Learning (QMAMCC-FL) technique. The presented QMAMCC-FL technique is designed a federated hybrid convolutional neural network with a gated recurrent unit (HCNN-GRU) model with a quantum Aquila optimization (QAO) algorithm. The QMAMCC-FL technique upgrades the global model via weight collection of the learned model, which is commonly used in FL. The proposed model can be employed to increase the performance of network communication and reduce the size of data transmitted from clients to servers such as smartphones and tablets. The experimental analysis of the QMAMCC-FL approach is tested, and the outcomes show better performance over other existing models.

A new optimal control strategy for the grid side converter (GSC) and rotor side converter (RSC) of a doubly-fed induction generator (DFIG) is developed in this paper using the Marine Predators algorithm (MPA). To accomplish this study, a comprehensive comparison between the suggested MPA-based control strategy and a well matured Particle Swarm Optimizer (PSO) to enhance transient stability of large-scale wind systems has been presented. MPA is used to determine the optimal gains of proportional-integral (PI) controllers for GSC and RSC to ensure a maximum power point tracking (MPPT) of a large-scale wind farm. The proposed optimal control strategy is analyzed and verified via a benchmark 9-MW DFIG wind farm using MATLAB/SIMULINK simulation. The attained results of the suggested MPA-PI-based controllers are compared to the conventional PI-based MPPT controllers to validate the efficacy of the developed optimal control strategy. The superiority of the proposed MPA-PI and PSO-PI-based optimal controllers over the traditional PI regulators towards enhancing the DFIG system dynamic performance has been proved. The presented MPA-PI-based control scheme has been succeeded in extracting the maximum power of the DFIG wind farm with a reduced settling time of about 1.8% and overshooting range 97% lower than the conventional controller.

Two modern methods of the energy management system (EMS) based on a modified cost function are addressed in this paper. Fuzzy logic (FL) and Harris Hawks Optimization (HHO) is implemented to achieve the optimal performance of seawater desalination plants (SWDP) within the minimum feed-in tariff (FiT). The technical difficulties involved in the variation of energy price from one time to another and the system parameters uncertainties. For example, the price of energy is higher at the peak time and the price is lower at normal times. Also, the peak time can change from one day to another day. The proposed management system can deal with these variations and uncertainty cases. The suggested EMS is achieved through a bidirectional electrical energy interchange approach ( $\pi $ -EEIA). The main concept behind the proposed $\pi $ -EEIA is how and when to inject the excess generated energy of renewable energy into the utility grid or charge the battery depending on the minimum dynamic cost criterion and vice versa. To accomplish this study a 700 m3/day SWDP located in Egypt fed on solar energy and a utility network has been constructed and analyzed. The system includes SWDP fed from a photovoltaic (PV) array as well as the utility grid in addition to a battery energy storage system (BESS). The main objective of this study is the management and coordination between the energy exchange process from the solar energy, the utility network, and BESS to provide sufficient electrical energy for SWDP within the minimum FiT. The system is constructed and validated using the MATLAB/SIMULINK™ software package. The proposed FL and HHO-based EMSs are investigated in the presence of the system uncertainties such as the change in the energy (excess or shortage) as well as the change in the energy price in the utility network (high or low) with (normal or peak) time. The attained results demonstrate that the proposed FL and HHO-based EMSs provide high dynamic performance and accurate coordination between various energy resources and BESS. The results show that FL-based EMS achieves a profit of 10.28 $\$ $ but the HHO-based EMS achieves a profit of 10.11 $\$ $ in the same period.

This paper proposes a new modified architecture for AC microgrid consisting of multiple grid-supporting master units (MUs) and multiple grid-feeding slave units (SUs). In this study, a coordinated four-layer hierarchal control (HC) approach is applied to the proposed structure for allowing the MUs, SUs and loads to be easily integrated as a microgrid and operated in both grid-integrated and standalone operation mode. The proposed structure of the AC microgrid enhances the system redundancy to prevent the single point of failure of MU and has more stability, efficiency, flexibility and reliability than the conventional structures. Furthermore, optimal design guidelines, based on a new hybrid Harries hawks and particle swarm optimization algorithm (H-HHOPSO) with the cooperation of different types of proposed multi-objective functions, are presented to fulfill the study objectives. The optimization constraints/objectives are employed for optimal parameters selection of HC controllers to improve the power quality, enhance dynamic and steady-state performance and guarantee a seamless transition between operation modes. To accomplish this work, the newly modified structure is modeled, constructed in MATLAB/SIMULINK and tested under the variations of generations and loads. This structure is also examined when the fault occurs at any one of the MUs and during the connecting and disconnecting of utility grid. This testing is to verify its flexibility and reliability, and confirm the effectiveness and robustness of the proposed optimal controllers. Additionally, the experimental work is carried out using the hardware-in-the-loop real-time emulation to prove the optimal controllers' feasibility. Finally, the experimental and simulation results are compared.

Active magnetic bearing (AMB) system has been recently employed widely as an ideal equipment for high-speed rotating machines. The inherent challenges to control the system include instability, nonlinearity and constricted range of operation. Therefore, advanced control technology is essential to optimize AMB system performance. This paper presents an application of model predictive control (MPC) based on linear parameter-varying (LPV) models to control an AMB system subject to input and state constraints. For this purpose, an LPV model representation is derived from the nonlinear dynamic model of the AMB system. In order to provide stability guarantees and since the obtained LPV model has a large number of scheduling parameters, the parameter set mapping (PSM) technique is used to reduce their number. Based on the reduced model, a terminal cost and an ellipsoidal terminal set are determined offline and included into the MPC optimization problem which are the essential ingredients for guaranteeing the closed-loop asymptotic stability. Moreover, for recursive feasibility of the MPC optimization problem, a slack variable is included into its cost function. The goal of the proposed feedback control system is twofold. First is to demonstrate high performance by achieving stable levitation of the rotor shaft as well as high capability of reference tracking without violating input and state constraints, which increases the overall safety of the system under disturbances effects. Second is to provide a computationally tractable LPVMPC algorithm, which is a substantial requirement in practice for operating the AMB system with high performance over its full range. Therefore, we propose an LPVMPC scheme with frozen scheduling parameter over the prediction horizon of the MPC. Furthermore, we demonstrate in simulation that such frozen LPVMPC can achieve comparable performance to a more sophisticated LPVMPC scheme developed recently and a standard NL MPC (NMPC) approach. Moreover, to verify the performance of the proposed frozen LPVMPC, a comparison with a classical controller, which is commonly applied to the system in practice, is provided.

The importance of image security in the field of medical imaging is challenging. Several research works have been conducted to secure medical healthcare images. Encryption, not risking loss of data, is the right solution for image confidentiality. Due to data size limitations, redundancy, and capacity, traditional encryption techniques cannot be applied directly to e-health data, especially when patient data are transferred over the open channels. Therefore, patients may lose the privacy of data contents since images are different from the text because of their two particular factors of loss of data and confidentiality. Researchers have identified such security threats and have proposed several image encryption techniques to mitigate the security problem. However, the study has found that the existing proposed techniques still face application-specific several security problems. Therefore, this paper presents an efficient, lightweight encryption algorithm to develop a secure image encryption technique for the healthcare industry. The proposed lightweight encryption technique employs two permutation techniques to secure medical images. The proposed technique is analyzed, evaluated, and then compared to conventionally encrypted ones in security and execution time. Numerous test images have been used to determine the performance of the proposed algorithm. Several experiments show that the proposed algorithm for image cryptosystems provides better efficiency than conventional techniques.

Power grid pattern is expected to evolve from generator-based power systems towards converter-based systems in the forthcoming decades. Therefore, grid-forming converters will be pertinent to interconnected power grids in pursuance of enhancement their resilience against disturbances. This paper introduces a new efficient damping control method for grid-forming converters that provides a smooth power modulation and an efficient damping response against frequency and voltage deviations. First, an averaged state-space representation for a grid forming application in dq synchronization frame is derived. Based on this model, a new hybrid damping controller, including the concept of state feedback control and PI control, is proposed to address the main issues in existing controllers. The state feedback controller is optimally designed using a linear-quadratic regulator (LQR) approach to optimize the system performance. Moreover, the PI controller is optimally designed using the pattern search algorithm. The proposed damping control method integrates optimally between the control loops through a mapping matrix to rapidly synchronize with the grid and efficiently damp the oscillations. Simulations are carried out to prove the proposed method robustness. Finally, a comparative study using controller hardware-in-the-loop (CHiL) is employed against conventional system to validate the proposed damping method.

Social media plays a significant role in disaster management by providing valuable data about affected people, donations, and help requests. Recent studies highlight the need to filter information on social media into fine-grained content labels. However, identifying useful information from massive amounts of social media posts during a crisis is a challenging task. In this paper, we propose I-AID, a multimodel approach to automatically categorize tweets into multi-label information types and filter critical information from the enormous volume of social media data. I-AID incorporates three main components: i) a BERT-based encoder to capture the semantics of a tweet and represent as a low-dimensional vector, ii) a graph attention network (GAT) to apprehend correlations between tweets’ words/entities and the corresponding information types, and iii) a Relation Network as a learnable distance metric to compute the similarity between tweets and their corresponding information types in a supervised way. We conducted several experiments on two real publicly-available datasets. Our results indicate that I-AID outperforms state-of-the-art approaches in terms of weighted average F1 score by +6% and +4% on the TREC-IS dataset and COVID-19 Tweets, respectively.

The wide application of microgrid concept leads to challenges for the traditional protection of distribution networks because of the changes in short circuit level and network topology during the two modes of microgrid operation. This paper proposes a promising solution for these problems by offering a new protection coordination scheme not affected by the variation of short circuit level or the changes in network topology. The proposed protection scheme is based on local measurements at relay location with low sampling frequency by computing the rate of change of fundamental voltage (ROCOV) to detect different fault types, identify the faulty zone accurately and guarantee robust coordination between primary and backup relays. The proposed coordination scheme can be achieved by optimizing either two settings for relay characteristic (time dial setting and pickup value) or four settings (time dial setting, pickup and the parameters that control the characteristic shape A & $B$ )). The proposed scheme is extensively tested using MATLAB simulations on the modified IEEE 14 bus meshed network embedded with synchronous/inverter-based distributed generation units under wide variations in operating conditions and short circuit levels for both grid-connected and islanded modes of operation. The achieved results confirm that the proposed coordination scheme can maintain the coordination between primary and backup relays for different fault locations, types and different topologies. It provides selective, reliable, and secured microgrid operation compared with conventional schemes, using fault current limiters and some other techniques discussed in the literature.

The tendency towards carbon dioxide reduction greatly stimulates the popularity of electric vehicles against conventional vehicles. However, electric vehicle chargers represent a huge electric burden, which affects the performance and stability of the grid. Various optimization methodologies have been proposed in literature to enhance the performance of the distribution grids. However, existing techniques handle the raised issues from individual perspectives and/or with limited scopes. Therefore, this paper aims to develop a distributed controller-based coordination scheme in both medium and low voltage networks to handle the electric vehicles’ charging impact on the power grid. The scope of this work covers improving the network voltage profile, reducing the total active and reactive power, reducing the load fluctuations and total charging cost, while taking into consideration the random arrivals/departures of electric vehicles and the vehicle owners’ preferred charging time zones with vehicle-to-grid technology. Simulations are carried out to prove the success of the proposed method in improving the performance of IEEE 31-bus 23 kV system with several 415 V residential feeders. Additionally, the proposed method is validated using Controller Hardware-in-the-Loop. The results show that the proposed method can significantly reduce the issues that appear in the electric power grid during charging with minor changes in the existing grid. The results prove the successful implementation of different types of charging, namely, ultra-fast, fast, moderate, normal and vehicle-to-grid charging with minimum charging cost to enhance the owner’s satisfaction level.

In this paper, the dynamics solution problem and the boundary control problem for the Timoshenko beam under uncertainties and exogenous disturbances are addressed. The dynamics of the Timoshenko beam are represented by one non-homogenous hyperbolic partial differential equation (PDE), one homogenous hyperbolic PDE, and three ordinary differential equations (ODEs). The authors suggest a method of lines (MOL) for obtaining the dynamics of the Timoshenko beam in the form of the ODE formula instead of the PDE formula. A global sliding mode boundary control (GSMBC) is designed for vibration reduction of the Timoshenko beam influenced by uncertainties, distributed disturbance, displacement boundary disturbance, and rotation boundary disturbance. Chattering phenomena are avoided by using exponential reaching law reinforced by a relay function. Along the time and position axis: a) the boundary displacements and rotation of the Timoshenko beam are converged to equilibrium; b) the distributed vibrations on both displacements and rotation axis of the Timoshenko are attenuated; c) influence of the exogenous disturbances and system parameters uncertainties are compensated. By using the Lyapunov direct approach, exponential convergence and robustness of the closed-loop system are guaranteed. Finally, simulations are carried out to show that the proposed GSMBC-based MOL scheme is effective for vanishing the vibrations of the Timoshenko beam under uncertainties and external disturbances.

In a multi-user multiple-input multiple-output (MU-MIMO) communication system, multiple RF (radio frequency) chains are generally connected corresponding to the antenna elements. However, the system complexity, hardware cost, and energy consumption are challenging while using many RF chains. In addition, an information-centric Internet of Things (IoT) will be significant to guarantee fast service demand for radio access in the fifth generation (5G) mmWave wireless systems. In this paper, a new beam training method based on a single RF chain is proposed to overcome these challenges by using the downlink–uplink and downlink–downlink beam training techniques to establish the elite users’ subset group which can be trained in a single training time slot. Additionally, the users’ subset mechanism is implemented by selecting an arbitrary function to guarantee the convergence. Moreover, the proposed techniques are compared with the conventional full search method regarding searching time, cost, and complexity. Different user subset functions are modeled, evaluated, and compared with the existing ones with respect to effective channel gain and average system capacity. The simulation results show that the proposed method can achieve a significant system performance, and the single RF chain is serviceable for a large number of users when connected with a maximum of 64 antenna elements at the BS. The proposed method can also be implemented in a massive MU-MIMO system by using multiple RF chains in which each RF chain is connected to a group of 64 antenna elements. This work is applicable in 5G mmWave communications, which require robust infrastructure to meet the future IoT service demand in 5G hot scenarios.

Human Body Communication (HBC), which utilizes the human body as a communication channel, is a promising communication method for wireless body area networks. In this paper, we use the deep-learning based approach to design and implement a new optimized architecture for HBC system with scalable date rates feature. The proposed transceiver is completely implemented using two deep neural networks, one represents the autoencoder for the transmitter and receiver, and the other for frame synchronization. The proposed autoencoder-based HBC improves block error rate by 2 dB compared to the conventional HBC design. In addition, low complexity modules for CRC encoder and decoder, Scrambler and Descrambler, and Preamble/SFD generator are proposed. Implemented under 45nm CMOS technology, the core size of the proposed design is 0.116 mm2, and the estimated power is 1.468 mW with a peak data rate of 5.25 Mbps. The energy efficiency (Eb) of the proposed design is 280 pJ/b that is over 3.5x better than the conventional HBC designs in literature.

Millimeter wave (mm-Wave) communications are emerging to meet the increasing demand for high transmission data rate in high user density areas. Meanwhile, the mm-Wave base station (BS) needs to employ a large number of antenna elements to increase the gain as well as serve a huge number of users. However, a vast number of antenna elements causes dimensionality problem in channel correlation matrix (rotation matrix). Therefore, we propose a novel codebook construction design based on CUR-decomposition technique to reduce the dimensionality problem. In this paper, the original correlation matrix is decomposed to the product of three low dimension matrices (C, U, and R). The new rotated codebook is then constructed by the new rotation matrix. Moreover, we evaluate the new decomposition matrix with the original matrix in terms of compression ratio and mismatch error. We also provide the achievable sum rate capacities for singular value decomposition, zero forcing, and a matched filter techniques to compare with the proposed method. Furthermore, the system capacity enhancement related to the number of antenna elements and the required feedback bits are analyzed. Simulation results show that the proposed method achieves much better system performance since the dimensionality problem is solved. The proposed method can be applied in the fifth generation massive antenna multi-user system with over a hundred antenna elements.