HOSIM: A high-order stochastic simulation algorithm for generating three-dimensional complex geological patterns

The three-dimensional high-order simulation algorithm HOSIM is developed to simulate complex non-linear and non-Gaussian systems. HOSIM is an alternative to the current MP approaches and it is based upon new high-order spatial connectivity measures, termed high-order spatial cumulants. The HOSIM algorithm implements a sequential simulation process, where local conditional distributions are generated using weighted orthonormal Legendre polynomials, which in turn define the so-called Legendre cumulants. The latter are high-order conditional spatial cumulants inferred from both the available data and training images. This approach is data-driven and reconstructs both high and lower-order spatial complexity in simulated realizations, while it only borrows from training images information that is not available in the data used. However, the three-dimensional implementation of the algorithm is computationally very intensive. To address his topic, the contribution of high-order conditional spatial cumulants is assessed in this paper through the number of Legendre cumulants with respect to the order of approximation used to estimate a conditional distribution and the number of data used within the respective neighbourhood. This leads to discarding the terms of Legendre cumulants with negligible contributions and allows an efficient simulation algorithm to be developed. The current version of the HOSIM algorithm is several orders of magnitude faster than the original version of the algorithm. Application and comparisons in a controlled environment show the excellent performance and efficiency of the HOSIM algorithm.     

Reversible circuit synthesis by genetic programming using dynamic gate libraries

We have defined a new method for automatic construction of reversible logic circuits by using the genetic programming approach. The choice of the gate library is 100% dynamic. The algorithm is capable of accepting all possible combinations of the following gate types: NOT TOFFOLI, NOT PERES, NOT CNOT TOFFOLI, NOT CNOT SWAP FREDKIN, NOT CNOT TOFFOLI SWAP FREDKIN, NOT CNOT PERES, NOT CNOT SWAP FREDKIN PERES, NOT CNOT TOFFOLI PERES and NOT CNOT TOFFOLI SWAP FREDKIN PERES. Our method produced near optimum circuits in some cases when a particular subset of gate types was used in the library. Meanwhile, in some cases, optimal circuits were produced due to the heuristic nature of the algorithm. We compared the outcomes of our method with several existing synthesis methods, and it was shown that our algorithm performed relatively well compared to the previous synthesis methods in terms of the output efficiency of the algorithm and execution time as well.     

Observer-based Adaptive Fuzzy Backstepping Tracking Control of Quadrotor Unmanned Aerial Vehicle Powered by Li-ion Battery

In this paper, an Adaptive Fuzzy Backstepping Control (AFBC) approach with state observer is developed. This approach is used to overcome the problem of trajectory tracking for a Quadrotor Unmanned Aerial Vehicle (QUAV) under wind gust conditions and parametric uncertainties. An adaptive fuzzy controller is directly used to approximate an unknown nonlinear backstepping controller which is based on the exact model of the QUAV. Besides, a state observer is constructed to estimate the states. The stability analysis of the whole system is proved using Lyapunov direct method. Uniformly Ultimately Bounded (UUB) stability of all signals in the closed-loop system is ensured. The proposed control method guarantees the tracking of a desired trajectory, attenuates the effect of external disturbances such as wind gust, and solves the problem of unavailable states for measurement. Extended simulation studies are presented to highlight the efficiency of the proposed AFBC scheme.     

Online linearization-based neural predictive control of air–fuel ratio in SI engines with PID feedback correction scheme

Model predictive control (MPC) frequently uses online identification to overcome model mismatch. However, repeated online identification does not suit the real-time controller, due to its heavy computational burden. This work presents a computationally efficient constrained MPC scheme using nonlinear prediction and online linearization based on neural models for controlling air–fuel ratio of spark ignition engine to its stoichiometric value. The neural model for AFR identification has been trained offline. The model mismatch is taken care of by incorporating a PID feedback correction scheme. Quadratic programming using active set method has been applied for nonlinear optimization. The control scheme has been tested on mean value engine model simulations. It has been shown that neural predictive control with online linearization using PID feedback correction gives satisfactory performance and also adapts to the change in engine systems very quickly.     

Formal verification of components assembly based on SysML and interface automata

We propose an approach which combines component SysML models and interface automata in order to assemble components and to verify formally their interoperability. So we propose to verify formally the assembly of components specified with the expressive and semi-formal modeling language, SysML. We specify component-based system architecture with SysML Block Definition Diagram, and the composition links between components with Internal Block Diagrams. Component’s protocols are specified with sequence diagrams, they are necessary to exploit interface automata formalism. Interface automata is a common Input Output (I/O) automata-based formalism intended to specify the signature and the protocol level of the component interfaces. We propose formal specifications for SysML semi-formal models in order to exploit interface automata approach. We also improve the interface automata approach by considering system architecture, specified with SysML, in the verification of components composition.     

Effects of imperfect channel sensing and estimation on the performance of cognitive radio systems

In realistic scenarios of cognitive radio (CR) systems, imperfect channel sensing may occur due to false alarms and miss detections. Channel estimation between the secondary user transmitter and another secondary user receiver is another challenge in CR systems, especially for frequency‐selective fading channels. In this context, this paper presents a study of the effects of imperfect channel sensing and channel estimation on the performance of CR systems. In particular, different methods of channel estimation are analyzed under channel sensing imperfections. Initially, a CR system model with channel sensing errors is described. Then, the expectation maximization (EM) algorithm is implemented in order to learn the channel fading coefficients. By exploiting the pilot symbols and the detected symbols at the secondary user receiver, we can estimate the channel coefficients. We further compare the proposed EM estimation algorithm with different estimation algorithms such as the least squares (LS) and linear minimum mean square error (LMMSE). The expressions of channel estimates and mean squared errors (MSE) are determined, and their dependencies on channel sensing uncertainty are investigated. Finally, to reduce the complexity of EM algorithm, a sub‐optimal algorithm is also proposed. The obtained results show that the proposed sub‐optimal algorithm provides a comparable bit error rate (BER) performance with that of the optimal one yet with less computational complexity.     

Unambiguous method for DLL BOC signals tracking

In this paper, we propose an efficient method for eliminating ambiguity in the acquisition and tracking processes of binary offset carrier (BOC)‐modulated signals. Our contribution is realized in two stages. The first one is based on the use, at the receiver end, of optimized locally generated subcarrier sequences to get a completely unambiguous composite correlation function (CF). This first stage is used to initialize and control the second stage of the proposed method designed on the basis of a maximum likelihood algorithm (MLA) to mitigate, specifically, multipath (MP) effect. In fact, the idea is to combine, in this second stage, the received signal (affected by noise and MP) with a set of locally generated ideal BOC(m,n)‐modulated signals, which are delayed and weighted by the MLA‐estimated parameters of delays, amplitudes, and phases of all the MP signals, to obtain an unambiguous CF with better performance. The simulation results, based on MATLAB tool, have demonstrated that the proposed method effectively eliminates the ambiguity problem. Besides, a comparative study with several methods has shown that the proposed method grants better performance than the most popular techniques.     

Probabilistic Verification Scenarios with Reduced Authentication Delay for Handoff Clients in Mesh Networks

Wireless Personal Communications - In this paper, we have proposed a secure handoff procedure by generating and assessing the tickets for each mesh client which are divided among various zones of...     

A triangular MIMO array antenna with a double negative metamaterial superstrate to enhance bandwidth and gain

Multiple‐input‐multiple‐output (MIMO) array antenna integrated with the double negative metamaterial superstrate is presented. The triangular metamaterial unit cell is designed by combining two triangular elements positioned in complementary on the same plane at different sizes. Such design with more gaps is used to excite rooms for more capacitance effects to shift the resonance frequency thus enlarging the bandwidth of the MIMO antenna. The unit cell is arranged in 7 × 7 periodic array created a superstrate metamaterial plane where the C_stray exists in parallel between the two consecutive cells. It is found that the existence of C_stray and gaps for each unit cells significantly influenced the bandwidth of the MIMO antenna. The higher value of the capacitance will lead to the negativity of permittivity. The superstrate plane is then located on top of the 4 × 2 MIMO with a gap of 5 mm. The integration resulted in improving the bandwidth to 12.45% (5.65‐6.4GHz) compared to only 3.49% bandwidth (5.91‐6.12GHz) of the MIMO antenna itself. Moreover, the negative permeability characteristic is created by a strong magnetic field between the complementary unit cells to have 14.05‐dBi peak gain. Besides that, the proposed antenna managed to minimize the mutual coupling and improve the mean effective gain, envelope correlation coefficient, and multiplexing efficiency.     

Analytical framework for dimensioning hierarchical WiMax–WiFi networks

Providing diverse, ubiquitous and cost-effective broadband services is a foremost challenge for the telecommunication community. Fixed WiMAX or IEEE 802.16d is one of the most promising radio access technologies, providing high performance similar to wired xDSL systems, yet superior to that of current 3G mobile technologies. Numerous deployment concepts are foreseen for WiMAX networks. They are designed to cover isolated areas, thus embodying an appealing solution for cellular networks or wireless backhaul for WiFi access. The latter concept is of interest in this paper that puts forward an analytical model based on the economical aspects to dimension hierarchical WiMax–WiFi networks. The proposed model consists in replacing a finite number of nodes by an equivalent continuum. Its key feature lies in accounting for the effect of interference as well as for the physical layer and channel characteristics in an easy and straightforward manner. On the one hand, the model takes into consideration frequency planning and scheduling aspects; and on the other hand, it provides tractable formulae of the end-user mean capacity and coverage probability in order to properly dimension the hybrid network. Last but not least, the economical facet of network planning is considered to unravel the design trade-offs between maximizing the service provider profit and satisfying the end user requirements in terms of performance.