Texas A&M University at Qatar

This letter addresses the problem of energy detection of an unknown signal over a multipath channel. It starts with the no-diversity case, and presents some alternative closed-form expressions for the probability of detection to those recently reported in the literature. Detection capability is boosted by implementing both square-law combining and square-law selection diversity schemes

This paper addresses to some of the latest contributions on the application of Finite Control Set Model Predictive Control (FCS-MPC) in Power Electronics. In FCS-MPC , the switching states are directly applied to the power converter, without the need of an additional modulation stage. The paper shows how the use of FCS-MPC provides a simple and efficient computational realization for different control objectives in Power Electronics. Some applications of this technology in drives, active filters, power conditioning, distributed generation and renewable energy are covered. Finally, attention is paid to the discussion of new trends in this technology and to the identification of open questions and future research topics.

A key challenge of future mobile communication research is to strike an attractive compromise between wireless network's area spectral efficiency and energy efficiency. This necessitates a clean-slate approach to wireless system design, embracing the rich body of existing knowledge, especially on multiple-input-multiple-ouput (MIMO) technologies. This motivates the proposal of an emerging wireless communications concept conceived for single-radio-frequency (RF) large-scale MIMO communications, which is termed as SM. The concept of SM has established itself as a beneficial transmission paradigm, subsuming numerous members of the MIMO system family. The research of SM has reached sufficient maturity to motivate its comparison to state-of-the-art MIMO communications, as well as to inspire its application to other emerging wireless systems such as relay-aided, cooperative, small-cell, optical wireless, and power-efficient communications. Furthermore, it has received sufficient research attention to be implemented in testbeds, and it holds the promise of stimulating further vigorous interdisciplinary research in the years to come. This tutorial paper is intended to offer a comprehensive state-of-the-art survey on SM-MIMO research, to provide a critical appraisal of its potential advantages, and to promote the discussion of its beneficial application areas and their research challenges leading to the analysis of the technological issues associated with the implementation of SM-MIMO. The paper is concluded with the description of the world's first experimental activities in this vibrant research field.

This paper gives an overview of medium-voltage (MV) multilevel converters with a focus on achieving minimum harmonic distortion and high efficiency at low switching frequency operation. Increasing the power rating by minimizing switching frequency while still maintaining reasonable power quality is an important requirement and a persistent challenge for the industry. Existing solutions are discussed and analyzed based on their topologies, limitations, and control techniques. As a preferred option for future research and application, an inverter configuration based on three-level building blocks to generate five-level voltage waveforms is suggested. This paper shows that such an inverter may be operated at a very low switching frequency to achieve minimum on-state and dynamic device losses for highly efficient MV drive applications while maintaining low harmonic distortion.

Solar photovoltaic (PV) energy has witnessed double-digit growth in the past decade. The penetration of PV systems as distributed generators in low-voltage grids has also seen significant attention. In addition, the need for higher overall grid efficiency and reliability has boosted the interest in the microgrid concept. High-efficiency PV-based microgrids require maximum power point tracking (MPPT) controllers to maximize the harvested energy due to the nonlinearity in PV module characteristics. Perturb and observe (P&O) techniques, although thoroughly investigated in previous research, still suffer from several disadvantages, such as sustained oscillation around the MPP, fast tracking versus oscillation tradeoffs, and user predefined constants. In this paper, a modified P&O MPPT technique, applicable for PV systems, is presented. The proposed technique achieves: first, adaptive tracking; second, no steady-state oscillations around the MPP; and lastly, no need for predefined system-dependent constants, hence provides a generic design core. A design example is presented by experimental implementation of the proposed technique. Practical results for the implemented setup at different irradiance levels are illustrated to validate the proposed technique.

Recent advances in flexible and stretchable electronics (FSE), a technology diverging from the conventional rigid silicon technology, have stimulated fundamental scientific and technological research efforts. FSE aims at enabling disruptive applications such as flexible displays, wearable sensors, printed RFID tags on packaging, electronics on skin/organs, and Internet-of-things as well as possibly reducing the cost of electronic device fabrication. Thus, the key materials components of electronics, the semiconductor, the dielectric, and the conductor as well as the passive (substrate, planarization, passivation, and encapsulation layers) must exhibit electrical performance and mechanical properties compatible with FSE components and products. In this review, we summarize and analyze recent advances in materials concepts as well as in thin-film fabrication techniques for high- k (or high-capacitance) gate dielectrics when integrated with FSE-compatible semiconductors such as organics, metal oxides, quantum dot arrays, carbon nanotubes, graphene, and other 2D semiconductors. Since thin-film transistors (TFTs) are the key enablers of FSE devices, we discuss TFT structures and operation mechanisms after a discussion on the needs and general requirements of gate dielectrics. Also, the advantages of high- k dielectrics over low- k ones in TFT applications were elaborated. Next, after presenting the design and properties of high- k polymers and inorganic, electrolyte, and hybrid dielectric families, we focus on the most important fabrication methodologies for their deposition as TFT gate dielectric thin films. Furthermore, we provide a detailed summary of recent progress in performance of FSE TFTs based on these high- k dielectrics, focusing primarily on emerging semiconductor types. Finally, we conclude with an outlook and challenges section.

In this note, the problem of second-order leader-following consensus by a novel distributed event-triggered sampling scheme in which agents exchange information via a limited communication medium is studied. Event-based distributed sampling rules are designed, where each agent decides when to measure its own state value and requests its neighbor agents broadcast their state values across the network when a locally-computed measurement error exceeds a state-dependent threshold. For the case of fixed topology, a necessary and sufficient condition is established. For the case of switching topology, a sufficient condition is obtained under the assumption that the time-varying directed graph is uniformly jointly connected. It is shown that the inter-event intervals are lower bounded by a strictly positive constant, which excludes the Zeno-behavior before the consensus is achieved. Numerical simulation examples are provided to demonstrate the correctness of theoretical results.

A multiple exp-function method to exact multiple wave solutions of nonlinear partial differential equations is proposed. The method is oriented towards ease of use and capability of computer algebra systems, and provides a direct and systematical solution procedure which generalizes Hirota's perturbation scheme. With help of Maple, an application of the approach to the $3+1$ dimensional potential-Yu-Toda-Sasa-Fukuyama equation yields exact explicit 1-wave and 2-wave and 3-wave solutions, which include 1-soliton, 2-soliton and 3-soliton type solutions. Two cases with specific values of the involved parameters are plotted for each of 2-wave and 3-wave solutions.

This paper presents a model predictive current control algorithm that is suitable for multilevel converters and its application to a three-phase cascaded H-bridge inverter. This control method uses a discrete-time model of the system to predict the future value of the current for all voltage vectors, and selects the vector which minimizes a cost function. Due to the large number of voltage vectors available in a multilevel inverter, a large number of calculations are needed, making difficult the implementation of this control in a standard control platform. A modified control strategy that considerably reduces the amount of calculations without affecting the system's performance is proposed. Experimental results for five- and nine-level inverters validate the proposed control algorithm.

This article addresses the adaptive event-triggered neural control problem for nonaffine pure-feedback nonlinear multiagent systems with dynamic disturbance, unmodeled dynamics, and dead-zone input. Radial basis function neural networks are applied to approximate the unknown nonlinear function. A dynamic signal is constructed to deal with the design difficulties in the unmodeled dynamics. Moreover, to reduce the communication burden, we propose an event-triggered strategy with a varying threshold. Based on the Lyapunov function method and adaptive neural control approach, a novel event-triggered control protocol is constructed, which realizes that the outputs of all followers converge to a neighborhood of the leader's output and ensures that all signals are bounded in the closed-loop system. An illustrative simulation example is applied to verify the usefulness of the proposed algorithms.

The ultra-dense network (UDN) has been considered as a promising candidate for future 5G networks to meet the explosive data demand. To realize UDN, a reliable, gigahertz bandwidth, and cost-effective backhaul connecting ultradense small-cell BSs and macrocell BS are prerequisite. Millimeter-wave can provide the potential gigabit-per-second traffic for wireless backhaul. Moreover, mmWave can easily be integrated with massive MIMO for improved link reliability. In this article, we discuss the feasibility of mmWave massive-MIMO-based wireless backhaul for 5G UDN, and the benefits and challenges are also addressed. In particular, we propose a digitally controlled phase shifter network (DPSN)-based hybrid precoding/combining scheme for mmWave massive MIMO, whereby the low-rank property of the mmWave massive MIMO channel matrix is leveraged to reduce the required cost and complexity of a transceiver with a negligible performance loss. One key feature of the proposed scheme is that the macrocell BS can simultaneously support multiple small-cell BSs with multiple streams for each small-cell BS, which is essentially different from conventional hybrid precoding/combining schemes, typically limited to single-user MIMO with multiple streams or multi-user MIMO with single stream for each user. Based on the proposed scheme, we further explore the fundamental issues of developing mmWave massive MIMO for wireless backhaul, and the associated challenges, insight, and prospects to enable mmWave massive-MIMO-based wireless backhaul for 5G UDN are discussed.

In this paper, the leader-following consensus problem of high-order multiagent systems via event-triggered control is discussed. A novel distributed event-triggered communication protocol based on state estimates of neighboring agents is proposed to solve the consensus problem of the leader-following systems. We first investigate the consensus problem in a fixed topology, and then extend to the switching topologies. State estimates in fixed topology are only updated when the trigger condition is satisfied. However, state estimates in switching topologies are renewed with two cases: 1) the communication topology is switched or 2) the trigger condition is satisfied. Clearly, compared to continuous-time interaction, this protocol can greatly reduce the communication load of multiagent networks. Besides, the event-triggering function is constructed based on the local information and a new event-triggered rule is given. Moreover, “Zeno behavior” can be excluded. Finally, we give two examples to validate the feasibility and efficiency of our approach.

In this study, we examine the effects of individual-level culture on the adoption and acceptance of e-learning tools by students in Lebanon using a theoretical framework based on the Technology Acceptance Model (TAM). To overcome possible limitations of using TAM in developing countries, we extend TAM to include subjective norms (SN) and quality of work life constructs as additional constructs and a number of cultural variables as moderators. The four cultural dimensions of masculinity/femininity (MF), individualism/collectivism, power distance and uncertainty avoidance were measured at the individual level to enable them to be integrated into the extended TAM as moderators and a research model was developed based on previous literature. To test the hypothesised model, data were collected from 569 undergraduate and postgraduate students using e-learning tools in Lebanon via questionnaire. The collected data were analysed using the structural equation modelling technique in conjunction with multi-group analysis. As hypothesised, the results of the study revealed perceived usefulness (PU), perceived ease of use (PEOU), SN and quality of work life to be significant determinants of students’ behavioural intention (BI) towards e-learning. The empirical results also demonstrated that the relationship between SN and BI was particularly sensitive to differences in individual-cultural values, with significant moderating effects observed for all four of the cultural dimensions studied. Some moderating effects of culture were also found for both PU and PEOU, however, contrary to expectations the effect of quality of work life was not found to be moderated by MF as some previous authors have predicted. The implications of these results to both theory and practice are explored in the paper.

The global evolution of wireless technologies and intelligent sensing devices are transforming the realization of smart cities. Among the myriad of use cases, there is a need to support applications whereby low-resource IoT devices need to upload their sensor data to a remote control centre by target hard deadlines; otherwise, the data becomes outdated and loses its value, for example, in emergency or industrial control scenarios. In addition, the IoT devices can be either located in remote areas with limited wireless coverage or in dense areas with relatively low quality of service. This motivates the utilization of UAVs to offload traffic from existing wireless networks by collecting data from time-constrained IoT devices with performance guarantees. To this end, we jointly optimize the trajectory of a UAV and the radio resource allocation to maximize the number of served IoT devices, where each device has its own target data upload deadline. The formulated optimization problem is shown to be mixed integer non-convex and generally NP-hard. To solve it, we first propose the high-complexity branch, reduce and bound (BRB) algorithm to find the global optimal solution for relatively small scale scenarios. Then, we develop an effective sub-optimal algorithm based on successive convex approximation in order to obtain results for larger networks. Next, we propose an extension algorithm to further minimize the UAV's flight distance for cases where the initial and final UAV locations are known a priori. We demonstrate the favourable characteristics of the algorithms via extensive simulations and analysis as a function of various system parameters, with benchmarking against two greedy algorithms based on distance and deadline metrics.

Photocatalytic water splitting is a sustainable technology for the production of clean fuel in terms of hydrogen (H2). In the present study, hydrogen (H2) production efficiency of three promising photocatalysts (titania (TiO2-P25), graphitic carbon nitride (g-C3N4), and cadmium sulfide (CdS)) was evaluated in detail using various sacrificial agents. The effect of most commonly used sacrificial agents in the recent years, such as methanol, ethanol, isopropanol, ethylene glycol, glycerol, lactic acid, glucose, sodium sulfide, sodium sulfite, sodium sulfide/sodium sulfite mixture, and triethanolamine, were evaluated on TiO2-P25, g-C3N4, and CdS. H2 production experiments were carried out under simulated solar light irradiation in an immersion type photo-reactor. All the experiments were performed without any noble metal co-catalyst. Moreover, photolysis experiments were executed to study the H2 generation in the absence of a catalyst. The results were discussed specifically in terms of chemical reactions, pH of the reaction medium, hydroxyl groups, alpha hydrogen, and carbon chain length of sacrificial agents. The results revealed that glucose and glycerol are the most suitable sacrificial agents for an oxide photocatalyst. Triethanolamine is the ideal sacrificial agent for carbon and sulfide photocatalyst. A remarkable amount of H2 was produced from the photolysis of sodium sulfide and sodium sulfide/sodium sulfite mixture without any photocatalyst. The findings of this study would be highly beneficial for the selection of sacrificial agents for a particular photocatalyst.

This paper is concerned with the synchronization control problem for a class of discrete time-delay complex dynamical networks under a dynamic event-triggered mechanism. For the efficiency of energy utilization, we make the first attempt to introduce a dynamic event-triggering strategy into the design of synchronization controllers for complex dynamical networks. A new discrete-time version of the dynamic event-triggering mechanism is proposed in terms of the absolute errors between control input updates. By constructing an appropriate Lyapunov functional, the dynamics of each network node combined with the introduced event-triggering mechanism are first analyzed, and a sufficient condition is then provided under which the synchronization error dynamics is exponentially ultimately bounded. Subsequently, a set of the desired synchronization controllers is designed by solving a matrix inequality. Finally, a simulation example is provided to verify the effectiveness of the proposed dynamic event-triggered synchronization control scheme.

To reduce information exchange requirements in smart grids, an event-triggered communication-based distributed optimization is proposed for economic dispatch. In this work, the θ-logarithmic barrier-based method is employed to reformulate the economic dispatch problem, and the consensus-based approach is considered for developing fully distributed technology-enabled algorithms. Specifically, a novel distributed algorithm utilizes the minimum connected dominating set (CDS), which efficiently allocates the task of balancing supply and demand for the entire power network at the beginning of economic dispatch. Further, an event-triggered communication-based method for the incremental cost of each generator is able to reach a consensus, coinciding with the global optimality of the objective function. In addition, a fast gradient-based distributed optimization method is also designed to accelerate the convergence rate of the event-triggered distributed optimization. Simulations based on the IEEE 57-bus test system demonstrate the effectiveness and good performance of proposed algorithms.

In this paper, we study the consensus problem for a class of linear multiagent systems, where the communication networks are directed. First, a dynamic event-triggering mechanism is introduced, including some existing static event-triggering mechanisms as its special cases. Second, based on the dynamic event-triggering mechanism, a distributed control protocol is developed, which ensures that all agents can reach consensus with an exponential convergence rate. Third, it is shown that, with the dynamic event-triggering mechanism, the minimum interevent time between any two consecutive triggering instants can be prolonged and no agent exhibits Zeno behavior. Finally, an algorithm is provided to avoid continuous communication when the dynamic event-triggering mechanism is implemented. The effectiveness of the results is confirmed through a numerical example.

This article studies the distributed observer-based event-triggered bipartite tracking control problem for stochastic nonlinear multiagent systems with input saturation. First, different from conventional observers, we construct a novel distributed reduced-order observer to estimate unknown states for the stochastic nonlinear systems. Then, an event-triggered mechanism with relative threshold is introduced to reduce the burden of communication. In addition, the bipartite tracking controller is proposed for stochastic multiagent systems by using fuzzy logic systems and the backstepping approach. Meanwhile, it is proved that the designed method can guarantee that all the signals in the closed-loop systems are bounded in probability, and the distributed consensus tracking errors can converge to a small neighborhood of the origin via the Lyapunov stability theory. Finally, a simulation example is given to prove the effectiveness of the designed scheme.

The H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control design problem is considered for nonlinear systems with unknown internal system model. It is known that the nonlinear H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control problem can be transformed into solving the so-called Hamilton-Jacobi-Isaacs (HJI) equation, which is a nonlinear partial differential equation that is generally impossible to be solved analytically. Even worse, model-based approaches cannot be used for approximately solving HJI equation, when the accurate system model is unavailable or costly to obtain in practice. To overcome these difficulties, an off-policy reinforcement leaning (RL) method is introduced to learn the solution of HJI equation from real system data instead of mathematical system model, and its convergence is proved. In the off-policy RL method, the system data can be generated with arbitrary policies rather than the evaluating policy, which is extremely important and promising for practical systems. For implementation purpose, a neural network (NN)-based actor-critic structure is employed and a least-square NN weight update algorithm is derived based on the method of weighted residuals. Finally, the developed NN-based off-policy RL method is tested on a linear F16 aircraft plant, and further applied to a rotational/translational actuator system.