Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Evolution of Design Method Against Random Waves Statistical Properties and Spectral of Sea Waves Transformation and Deformation of Random Sea Waves Design of Breakwaters Design of Coastal Dikes and Seawalls Probabilistic Design of Harbor Facilities Harbor Tranquility and Vessel Mooring Hydraulic Model Tests with Random Waves Theoretical Description of Random Sea Waves Statistical Theory of Irregular Waves Techniques of Random Wave Analysis 2D Computation of Wave Transformation with Random Breaking and Nearshore Currents Statistical Analysis of Extreme Waves Prediction and Control of Beach Deformation Processes.

Random seas and design of maritime structures

This paper proposes an innovative tuning approach for fuzzy control systems (CSs) with a reduced parametric sensitivity using the Grey Wolf Optimizer (GWO) algorithm. The CSs consist of servo system processes controlled by Takagi–Sugeno–Kang proportional-integral fuzzy controllers (TSK PI-FCs). The process models have second-order dynamics with an integral component, variable parameters, a saturation, and dead-zone static nonlinearity. The sensitivity analysis employs output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The GWO algorithm is used in solving the optimization problems, where the objective functions include the output sensitivity functions. GWO's motivation is based on its low-computational cost. The tuning approach is validated in an experimental case study of a position control for a laboratory nonlinear servo system, and TSK PI-FCs with a reduced process small time constant sensitivity are offered.

Grey Wolf Optimizer Algorithm-Based Tuning of Fuzzy Control Systems With Reduced Parametric Sensitivity

Pressure-swing distillation (PSD) is widely used as an efficient method for separating pressure-sensitive azeotropic mixtures in industrial processes. Remarkably, PSD can achieve pure products without introducing a third component compared with extractive distillation and azeotropic distillation. Heat integration into PSD can save energy and reduce operating costs, thus relieving the continuous growth of energy consumption in the distillation industry. This review paper describes the development of this widely used distillation technique, including all of the main aspects related to thermodynamic analysis, Quantitative structure property relationship (QSPR), process design, process intensification, and dynamic control. Based on the foundation of research, further development of PSD is proposed for separating multi-component azeotropic mixtures and exploring the process design and dynamic control from QSPR, aiming at promoting the industrial application of this environmentally friendly and well-known separation technique from multi-scale analysis.

Insight into pressure-swing distillation from azeotropic phenomenon to dynamic control

1.Introduction 2.Fundamental Modeling and Control of Unmanned Small-Scale and Mi-niature Helicopters 3.Autonomous Control of a Mini Quadrotor Vehicle Using LQG Controllers 4.Modeling and Control of an Autonomous Quad-Tilt-Wing (QTW) UAV 5.Linearlization and Identification of Helicopter Model for Hierarchical Control Design 6.Analysis of the Autorotation Maneuver in Small-Scale Helicopters and Application for Emergency Landing 7.Autonomous Acrobatic Flight based on Feedforward Sequence Control for Small Unmanned Helicopter 8.Mathematical Modeling and Nonlinear Control of VTOL Aerial Vehicles 9.Formation Flight Control of Multiple Autonomous Helicopters Using Predictive Control 10.Guidance and Navigation Systems for Small Aerial Robots 11.Design and Implementation of a Low-Cost Attitude Quaternion Sensor 12.Vision-Based Navigation and Visual Servoing of Mini Flying Machines 13.Autonomous Indoor Flight and Precise Auto-Landing Using Infrared and Ultrasonic Sensors

Autonomous flying robots : unmanned aerial vehicles and micro aerial vehicles

Most real-world dynamical systems are often involving continuous behaviors and discrete events, in this case, they are called hybrid dynamical systems (HDSs). To properly model this kind of systems, it is necessary to consider both the continuous and the discrete aspects of its dynamics. In this paper, a modeling framework based on the hybrid automata (HA) approach is proposed. This hybrid modeling framework allows combining the multi-state models of the system, described by nonlinear diﬀerential equations, with the system’s discrete dynamics described by ﬁnite state machines. To attest to the efficiency of the proposed modeling framework, its application to a two-tank hybrid system (TTHS) is presented. The TTHS studied is a typical benchmark for HDSs with four operating modes. The MATLAB Simulink and Stateflow tools are used to implement and simulate the hybrid model of the TTHS. Different simulations results demonstrate the efficiency of the proposed modeling framework, which allows us to appropriately have a complete model of an HDS.

Modeling and simulation of the two-tank system within a hybrid framework

In this paper, a new method for on-line optimisation of a fuzzy controller is presented. It is based on a Sugeno fuzzy controller which is on-line tuned to adjust its parameters, using a gradient method. The purpose of the proposed approach is to give an algorithmic way to determine the parameters of such a controller, without using human knowledge, as it is usual in the design of fuzzy controllers, while preserving a human understanding of the fuzzy controller's action. An illustrative example is given by the application of the developed method to the control of a second order linear system.

Joseph Haggège

Papers

Modeling and simulation of the two-tank system within a hybrid framework

A new approach for on-line optimisation of a fuzzy controller