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

Bilateral teleoperation: An historical survey

Summary) The abstract should follow the structure of the article (relevance, degree of exploration of the problem, the goal, the main results, conclusion) and characterize the theoretical and practical significance of the study results. The abstract should not contain wording echoing the title, cumbersome grammatical structures and abbreviations. The text should be written in scientific style. The volume of abstracts (summaries) depends on the content of the article, but should not be less than 250 words. All abbreviations must be disclosed in the summary (in spite of the fact that they will be disclosed in the main text of the article), references to the numbers of publications from reference list should not be made. The sentences of the abstract should constitute an integral text, which can be made by use of the words “consequently”, “for example”, “as a result”. Avoid the use of unnecessary introductory phrases (eg, “the author of the article considers...”, “The article presents...” and so on.)

/pdf/ministry-of-education-and-science-of-the-russian-federation-7m2jn0xnm1.pdf

Ministry of Education and Science of the Russian Federation

One of the most important design problems for multi-UAV (Unmanned Air Vehicle) systems is the communication which is crucial for cooperation and collaboration between the UAVs. If all UAVs are directly connected to an infrastructure, such as a ground base or a satellite, the communication between UAVs can be realized through the in-frastructure. However, this infrastructure based communication architecture restricts the capabilities of the multi-UAV systems. Ad-hoc networking between UAVs can solve the problems arising from a fully infrastructure based UAV networks. In this paper, Flying Ad-Hoc Networks (FANETs) are surveyed which is an ad hoc network connecting the UAVs. The differences between FANETs, MANETs (Mobile Ad-hoc Networks) and VANETs (Vehicle Ad-Hoc Networks) are clarified first, and then the main FANET design challenges are introduced. Along with the existing FANET protocols, open research issues are also discussed.

Flying Ad-Hoc Networks (FANETs)

Aircraft wings are a compromise that allows the aircraft to fly at a range of flight conditions, but the performance at each condition is sub-optimal. The ability of a wing surface to change its geometry during flight has interested researchers and designers over the years as this reduces the design compromises required. Morphing is the short form for metamorphose; however, there is neither an exact definition nor an agreement between the researchers about the type or the extent of the geometrical changes necessary to qualify an aircraft for the title ‘shape morphing.’ Geometrical parameters that can be affected by morphing solutions can be categorized into: planform alteration (span, sweep, and chord), out-of-plane transformation (twist, dihedral/gull, and span-wise bending), and airfoil adjustment (camber and thickness). Changing the wing shape or geometry is not new. Historically, morphing solutions always led to penalties in terms of cost, complexity, or weight, although in certain circumstances, thes...

A Review of Morphing Aircraft

Tools for quantifying teleoperation system performance and stability when communication delays are present are provided A general multivariable system architecture is utilized which includes all four-types of data transmission between master and slave: force and velocity in both directions It is shown that a proper use of an four channels is of critical importance in achieving high performance telepresence in the sense of accurate transmission of task impedances to the operator It is also shown that transparency and robust stability (passivity) are conflicting design goals in teleoperation systems The analysis is illustrated by comparing transparency and stability in two common architectures, as well as a recent passivated approach and a new transparency-optimized architecture, using simplified one-degree-of-freedom examples >

Stability and transparency in bilateral teleoperation

This paper presents a control structure for cooperative stand-off line-of-sight tracking of a moving target by a team of unmanned aircraft based on a Lyapunov guidance vector field that produces stable convergence to a circling limit cycle behavior. A guidance vector field is designed for a stationary target and then modified with a correction term that accounts for a moving target and constant background wind. Cooperative tracking by multiple unmanned aircraft is achieved through additional phasing, also with a Lyapunov stability analysis. Convoy protection, in which the unmanned aircraft must scout an area ahead of the moving target, is performed by extending the cooperative stand-offline-of-sight limit cycle attractor along the direction of travel. Simulation results demonstrate the behavior of the algorithms as well as the improvement that results from cooperation. Finally, simulations of a larger cooperative search, acquisition, and tracking scenario are described that illustrate the use of the cooperative standoff line-of-sight and convoy protection controllers in a realistic application.

Coordinated Standoff Tracking of Moving Targets Using Lyapunov Guidance Vector Fields

Various implementations of impedance control have been suggested, usually based on idealized models of the physical system. This work considers the nonideal, practical effects of computation and/or communication delays and manipulator dynamics on the behavior of two primary approaches to impedance control. The results are cast in the form of stability boundaries, i.e. the relationships between desired impedance parameters which cause marginally stable behavior in the overall system. These stability boundaries are compared for the two primary implementations, and relative benefits of each approach are discussed. These comparisons provide the basis for quantitative tradeoffs, allowing selection of control implementation approaches suited for particular manipulators or allowing quantitative decisions to be made in manipulator system design. >

Impedance control stability properties in common implementations

General techniques for constructing vector fields for unmanned aircraft guidance are provided that incorporate Lyapunov stability properties to produce simple, globally stable vector fields in three dimensions. Use of these fields to produce circular loiter pattern attractors is illustrated, along with a simple switching algorithm to enable following of arbitrary way point sequences. Alternatively, attractor shape variations are developed by warping the circular loiter, preserving global stability guarantees, and accurate path tracking. An example of this technique is provided that produces a racetrack loiter pattern, and three different variations in the warping technique are compared. Finally, tracking of the vector field is considered, using Lyapunov techniques to show global stability of heading and path position for several types of tracking control laws that are compatible with low cost unmanned aircraft avionics.

Lyapunov Vector Fields for Autonomous Unmanned Aircraft Flight Control

An airborne wireless sensor network (WSN) composed of bird-sized micro aerial vehicles (MAVs) enables low cost high granularity atmospheric sensing of toxic plume behavior and storm dynamics, and provides a unique three-dimensional vantage for monitoring wildlife and ecological systems. This paper describes a complete implementation of our SensorFlock airborne WSN, spanning the development of our MAV airplane, its avionics, semi-autonomous flight control software, launch system, flock control algorithm, and wireless communication networking between MAVs. We present experimental results from flight tests of flocks of MAVs, and a characterization of wireless RF behavior in air-to-air communication as well as air-to-ground communication.

Dale Lawrence

Papers

Stability and transparency in bilateral teleoperation

Coordinated Standoff Tracking of Moving Targets Using Lyapunov Guidance Vector Fields

Impedance control stability properties in common implementations

Lyapunov Vector Fields for Autonomous Unmanned Aircraft Flight Control

SensorFlock: an airborne wireless sensor network of micro-air vehicles