Iterative scheme for MS image pansharpening based on the combination of multi-resolution decompositions

Image pansharpening in the remote-sensing domain may be defined as the technique of extracting high-resolution details from the panchromatic (PAN) image and injecting them into the multispectral (MS) one in a way to preserve the spectral signature and improve the spatial resolution. In this article, the authors propose an image fusion framework that tries to derive sharpened MS image such that: (i) when decimated taking into account the imagery system Modulation Transfer Function (MTF), it equals the original MS image; (ii) when decomposed using discrete wavelet transform (DWT), its geometrical details are those of the PAN image weighted by the compatibility PAN/MS. Indeed, MS sharpening is carried out in two steps. First, pre-pansharpened MS image is obtained using inverse DWT taking as approximations those of the upsampled original MS image and as details those of PAN (to reduce spectral distortion, PAN detail injection is performed proportionally to the similarity PAN/MS). Second, to satisfy (i) and to remove the PAN-MS disagreement, an iteration algorithm (alternatively corrects approximations and details) has been proposed. The proposed approach is designed in two versions inspired by the Generalized Laplacian Pyramid (GLP) and the Gram–Schmidt (GS) transformation, respectively.

To validate our approach, Pléiades-1A, Geoeye-1, and Landsat Enhanced Thematic Mapper Plus (ETM+) images are tested. The results of qualitative and quantitative scores are presented and discussed. Compared to well-known techniques, our approach shows generally better results, particularly the one based on GLP formalism.     

A splitting-based finite element method for the Biot poroelasticity system

In this work, we propose a finite element method for solving the linear poroelasticity equations. Both displacement and pressure are approximated by continuous piecewise polynomials. The proposed method is sequential, leading to decoupled smaller linear systems compared to the systems resulting from a fully implicit finite element approach. A priori error estimates are derived. Numerical results validate the theoretical convergence rates.     

A New Adaptive Neuro-sliding Mode Control for Gantry Crane

This paper presents a new adaptive neuro-sliding mode control for gantry crane as varying rope length. This control method derived from combining the sliding surfaces of three subsystem of the gantry crane (trolley position, rope length, anti-swing) to draw out two system sliding surfaces: the trolley position with the anti-swing and the rope length and the anti-swing. On the based of the sliding mode control principle, drawn out the equivalent controller and the switching controller for gantry crane. But due to the uncertain parameters-nonlinear model of gantry crane with the bound disturbances, combining the neural approximate method, defined the neural controller and the compensation controller for the difference between the equivalent controller and the neural controller for two system control inputs: trolley position and rope length. The adaptive control laws for these controllers were deduced from Lyapunov’s stable criteria to asymptotically stabilize the sliding surfaces. Simulation studies are performed to illustrate the effectiveness of the proposed control.     

An improved volleyball premier league algorithm based on sine cosine algorithm for global optimization problem

Volleyball premier league (VPL) simulating some phenomena of volleyball game has been presented recently. This powerful algorithm uses such racing and interplays between teams within a season. Furthermore, the algorithm imitates the coaching procedure within a game. Therefore, some volleyball metaphors, including substitution, coaching, and learning, are used to find a better solution prepared by the VPL algorithm. However, the learning phase has the largest effect on the performance of the VPL algorithm, in which this phase can lead to making the VPL stuck in optimal local solution. Therefore, this paper proposed a modified VPL using sine cosine algorithm (SCA). In which the SCA operators have been applied in the learning phase to obtain a more accurate solution. So, we have used SCA operators in VPL to grasp their advantages resulting in a more efficient approach for finding the optimal solution of the optimization problem and avoid the limitations of the traditional VPL algorithm. The propounded VPLSCA algorithm is tested on the 25 functions. The results captured by the VPLSCA have been compared with other metaheuristic algorithms such as cuckoo search, social-spider optimization algorithm, ant lion optimizer, grey wolf optimizer, salp swarm algorithm, whale optimization algorithm, moth flame optimization, artificial bee colony, SCA, and VPL. Furthermore, the three typical optimization problems in the field of designing engineering have been solved using the VPLSCA. According to the obtained results, the proposed algorithm shows very reasonable and promising results compared to others.

     

Stability and robustness of fuzzy adaptive control of nonlinear systems

In this paper, we propose a new approach that guarantees the stability and robustness of an adaptive control law of a nonlinear system.The control diagram proposed contains a Takagi–Sugeno–Kang fuzzy controller (TSK-FC) and a training block allowing the online adaptation of the FC parameters. The adaptation algorithm used is based on the gradient with minimization of the quadratic error between the system output and that desired by using the direct method of Lyapunov. However, our approach considers the gradient step of each adaptive FC parameter to be bound. This approach was applied to the control of an inverted pendulum. The results obtained confirm well the validity of such an adaptation especially the guarantee of the pendulum stability and the robustness of its control with respect to the disturbances introduced on the FC parameters and the pendulum itself.     

Deep Semantic Structure of Natural Language

This paper presents the result of research of deep structure of natural language. The main result attained is the existence of a deterministic mathematical model that relates phonetics to associated mental images starting from the simplest linguistic units in agreement with the human response to different acoustic stimuli. Moreover, there exists two level hierarchy for natural language understanding. The first level uncovers the conceptual meaning of linguistic units, and hence forming a corresponding mental image. At the second level the operational meaning is found to suit, context, pragmatics, and world knowledge. This agrees with our knowledge about human cognition. The resulting model is parallel, hierarchical but still concise to explain the speed of natural language understanding.     

A pure geometric theory of gravity and a material distribution

A field theory is constructed in the context of parameterized absolute parallelism geometry. The theory is shown to be a pure gravity one. It is capable of describing the gravitational field and a material distribution in terms of the geometric structure of the geometry used (the parallelization vector fields). Three tools are used to attribute physical properties to the geometric objects admitted by the theory. Poisson and Laplace equations are obtained in the linearized version of the theory. The spherically symmetric solution of the theory, in free space, is found to coincide with the Schwarzschild exterior solution of general relativity. The theory respects the weak equivalence principle in free space only. Gravity and the material distribution are not minimally coupled.     

Photostable liquid‐crystal guest‐host nano‐materials for display applications

Abstract— We theoretically modeled the optical plasmon absorption of anisotropic metallic nanoparticles in a liquid‐crystal host medium. Metallic nanorods and spheroids act as pleochroic dopants with virtually unlimited photostability. Calculations predict that full‐color displays based on nanorod orientation driven by the transition from homogeneous to homeotropic LC alignment are feasible. These displays are expected to have large viewing angles without the need for polarizers or LC anchoring surfaces.     

IoT-Enabled Autonomous System Collaboration for Disaster-Area Management

Timely investigating post-disaster situations to locate survivors and secure hazardous sources is critical, but also very challenging and risky. Despite first responders putting their lives at risk in saving others, human-physical limits cause delays in response time, resulting in fatality and property damage. In this paper, we proposed and implemented a framework intended for creating collaboration between heterogeneous unmanned vehicles and first responders to make search and rescue operations safer and faster. The framework consists of unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), a cloud-based remote control station (RCS). A light-weight message queuing telemetry transport (MQTT) based communication is adopted for facilitating collaboration between autonomous systems. To effectively work under unfavorable disaster conditions, antenna tracker is developed as a tool to extend network coverage to distant areas, and mobile charging points for the UAVs are also implemented. The proposed framework’s performance is evaluated in terms of end-to-end delay and analyzed using architectural analysis and design language (AADL). Experimental measurements and simulation results show that the adopted communication protocol performs more efficiently than other conventional communication protocols, and the implemented UAV control mechanisms are functioning properly. Several scenarios are implemented to validate the overall effectiveness of the proposed framework and demonstrate possible use cases.