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

Shigaraki UAV-Radar Experimen: ShUREX 2015

A new optical sensor system is presented that measures a reference vector direction relative to the body frame of a small unmanned aircraft system (sUAS) at medium ranges in daylight conditions with high precision and without drift using a low-power, nonlaser, eye-safe optical beacon. The beacon operates as a monochromatic, time-modulated optical source, and the airborne sensor uses both optical band-pass filtering and frequency domain signal processing to separate the beacon signal from reflected and scattered sunlight incident on the sensor's aperture, thus providing increased sensitivity. Characterization of the prototype sensor shows that the reference-vector direction angles can be measured with an accuracy of better than 0.5% of the field of view, resulting in 0.1° accuracy over a 30 degree range, at a measurement update rate of 200 Hz. This result is based on an operating range of 134 m using a 1 W beacon with a 9° beamwidth. The prototype system was integrated into and tested on a small UAS. Flight test results are presented comparing the prototype sensor system's output to an estimate of the reference-vector direction produced by fusing inertial measurement and GPS information, thus demonstrating how this sensor system offers a method of validating sUAS attitude estimation systems. The sensor system may also be used to augment sUAS attitude estimation systems, or for other purposes such as in a precision sUAS landing system or for state estimation in GPS-denied environments.

Optical Beacon Sensor for Small Unmanned Aerial System State Estimation

TKE dissipation rate can be estimated from VHF/UHF Doppler radars. The currently used model for stably stratified conditions seems to be challenged by comparisons with UAV measurements. An alternative model derived for weakly stable conditions (Basu and Holtslag, EFM, 21, 1273, 2021) provides better agreements for shear-generated turbulent layers.

Dale Lawrence

Papers

Shigaraki UAV-Radar Experimen: ShUREX 2015

Optical Beacon Sensor for Small Unmanned Aerial System State Estimation

Turbulence Kinetic Energy dissipation rate estimated from clear air radar data: comparisons of models in light of DataHawk UAV measurements