Search

Due to the widely spreading of fake news utilizing image manipulation and its bad effects, this paper investigates efficient image contents verification and manipulation detection algorithm performance in presence different attacks and noisy wireless channel. The presented algorithm works based on making horizontal scanning of the image blocks after segmenting it to upper and lower partition, every one of them is divided to equal number of small blocks. These blocks marks the opposite block in another image petitions utilizing different transforms, DFT, DCT, WHT, and DWT transforms. This approach is evaluated and tested in presence different attacks and over the wireless noisy channel for measuring the reliability and robustness of the presented algorithm. The WHT, DCT and DWT based algorithm performed good. The computer simulations reveled the DWT-based approach provides images with littlie quality improving. The algorithm performance over the AWGN channel is bad at the low SNR. For solve this problem the simple and less-complex error control schemes have been utilized to decrease the required SNR for achieving accepted quality of the received image. The merging algorithm is proposed also based on utilizing the mark encrypted image and encrypt marked images verifications approaches. Finally, the computer simulations proved the reliability and robustness of the DWT-based approach and its high detection sensitivity for any image manipulation in the different testing scenarios. [ABSTRACT FROM AUTHOR]

The paper presents the stress-strain state simulation of the compound technical structure - vulcanizer diaphragm. A set of working plates in the form of a circular plate with the thickness discretely varied in the radial direction is modeled. An algorithm to solve the contact problem is developed. The obtained calculation results are compared with the experimental ones to demonstrate their practical use. [ABSTRACT FROM AUTHOR]

Imagine coating buildings and bridges with smart particles (also coined smart paint) that monitor structural integrity and sense and report on traffic and wind loads, leading to technology that could do such inspection jobs faster and cheaper and increase safety at the same time. In this paper, we study the problem of uniformly coating objects of arbitrary shape in the context of self-organizing programmable matter, i.e., programmable matter which consists of simple computational elements called particles that can establish and release bonds and can actively move in a self-organized way. Particles are anonymous, have constant-size memory, and utilize only local interactions in order to coat an object. We continue the study of our universal coating algorithm by focusing on its runtime analysis, showing that our algorithm terminates within a linear number of rounds with high probability. We also present a matching linear lower bound that holds with high probability. We use this lower bound to show a linear lower bound on the competitive gap between fully local coating algorithms and coating algorithms that rely on global information, which implies that our algorithm is also optimal in a competitive sense. Simulation results show that the competitive ratio of our algorithm may be better than linear in practice. [ABSTRACT FROM AUTHOR]

In this paper, the joint transceiver design issue is investigated for multi-cell systems with multiple mobile terminals (MTs). An optimization problem is formulated based on the criterion of mean squared error minimization for the joint design of the cooperative precoder of all multi-input multi-output (MIMO) cells and the cooperative decoder of all MIMO MTs. With a fixed decoder, the optimum precoder is first derived in a closed-form expression, where no exchange of data streams among all of the MIMO cells is required due to the limited capability of the backhaul. Subsequently, an optimum decoder is then obtained in an analytical expression with the fixed precoder. Based on these two expressions, an iterative algorithm with guaranteed convergence is finally developed to carry out the joint optimization of the cooperative precoders and decoders. Simulation results demonstrating the effectiveness of the proposed algorithm are also provided. [ABSTRACT FROM AUTHOR]

Coupling sensors in a sensor network with mobility mechanism can boost the performance of wireless sensor networks (WSNs). In this paper, we address the problem of self-deploying mobile sensors to reach high coverage. The problem is modeled as a multi-objective optimization that simultaneously minimizes two contradictory parameters; the total sensor moving distance and the total uncovered area. In order to resolve the aforementioned deployment problem, this study investigates the use of biologically inspired mechanisms, including evolutionary algorithms and swarm intelligence, with their state-of-the-art algorithms. Unlike most of the existing works, the coverage parameter is expressed as a probabilistic inference model due to uncertainty in sensor readings. To the best of our knowledge, probabilistic coverage of mobile sensor networks has not been addressed in the context of multi-objective bio-inspired algorithms. Performance evaluations on deployment quality and deployment cost are measured and analyzed through extensive simulations, showing the effectiveness of each algorithm under the developed objective functions. Simulations reveal that only one multi-objective evolutionary algorithm; the so-called multi-objective evolutionary algorithm with decomposition survives to effectively tackle the probabilistic coverage deployment problem. It gathers more than 78 % signals from all of the targets (and in some cases reaches 100 % certainty). On the other hand, non-dominated sorting genetic algorithm II, multi-objective particle swarm optimization, and non-dominated sorting particle swarm optimization show inferior performance down to 16-32 %, necessitating further modifications in their internal mechanisms. [ABSTRACT FROM AUTHOR]

The harmony search (HS) method is a popular meta-heuristic optimization algorithm, which has been extensively employed to handle various engineering problems. However, it sometimes fails to offer a satisfactory convergence performance under certain circumstances. In this paper, we propose and study a hybrid HS approach, HS-PBIL, by merging the HS together with the population-based incremental learning (PBIL). Numerical simulations demonstrate that our HS-PBIL is well capable of outperforming the regular HS method in dealing with nonlinear function optimization and a practical wind generator optimization problem. [ABSTRACT FROM AUTHOR]

The purpose of this work is to estimate target coordinates and velocity. Target tracking is performed by an autonomous underwater vehicle (AUV). The observer is equipped with the direction finder that measures the azimuth and elevation angles of the target and the target range. Such problems are normally solved with the aid of non-linear filters due to nonlinearity of measurements. However, most filters produce estimations with unknown bias in the absence of estimations of accuracy. An important problem is related to unobservability of the target. A pseudomeasurement method is proposed to transform the problem of estimation into a linear problem. Such an approach makes possible to estimate coordinates and velocity of a moving target and solve the problem of unobservability. Results of computer simulation prove efficiency of the proposed algorithm in the problem of passive target tracking when only angles are measured. [ABSTRACT FROM AUTHOR]

We consider the augmented Lagrangian method (ALM) as a solver for the fused lasso signal approximator (FLSA) problem. The ALM is a dual method in which squares of the constraint functions are added as penalties to the Lagrangian. In order to apply this method to FLSA, two types of auxiliary variables are introduced to transform the original unconstrained minimization problem into a linearly constrained minimization problem. Each updating in this iterative algorithm consists of just a simple one-dimensional convex programming problem, with closed form solution in many cases. While the existing literature mostly focused on the quadratic loss function, our algorithm can be easily implemented for general convex loss. We also provide some convergence analysis of the algorithm. Finally, the method is illustrated with some simulation datasets. [ABSTRACT FROM AUTHOR]

We present a new generic sequential importance sampling algorithm, called stochastic enumeration (SE) for counting #P-complete problems, such as the number of satisfiability assignments and the number of perfect matchings (permanent). We show that SE presents a natural generalization of the classic one-step-look-ahead algorithm in the sense that it: Runs in parallel multiple trajectories instead of a single one; Employs a polynomial time decision making oracle, which can be viewed as an n-step-look-ahead algorithm, where n is the size of the problem. Our simulation studies indicate good performance of SE as compared with the well-known splitting and SampleSearch methods. [ABSTRACT FROM AUTHOR]

The authors present finite difference algorithms for solving nonstationary problems on dynamics of elastic media and structures, and perform numerical modeling of the following applied problems: stroke on an elastic half-plane by a rigid die; shock piling; generation of a pendulum wave in a blocky medium by a local impulse effect. One of the goals of the presented modeling is driving the problem solutions with minimized influence of spurious effects inherent to numerical approximation. Finally, the authors compare the results obtained in different models of elastic media. [ABSTRACT FROM AUTHOR]