The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.

/pdf/on-road-vehicle-detection-a-review-4iw469j6iz.pdf

On-road vehicle detection: a review

This paper formulates a general analytic approach to the theory of microwave generation in magnetic nano-structures driven by spin-polarized current and reviews analytic results obtained in this theory. The proposed approach is based on the universal model of an auto-oscillator with negative damping and nonlinear frequency shift. It is demonstrated that this universal model, when applied to the case of a spin-torque oscillator (STO) based on a current-driven magnetic nano-pillar or nano-contact, gives adequate description of most of the experimentally observed properties of STO. In particular, the model describes the power and frequency of the generated microwave signal as functions of the bias current and magnetic field, predicts the magnitude and properties of the generation linewidth, and explains the STO behavior under the influence of periodic and stochastic external signals: frequency modulation, phase-locking to external signals, mutual phase-locking in an array of STO, broadening of the generation linewidth near the generation threshold, etc. The proposed nonlinear auto-oscillator theory is rather general and can be used not only for the development of practical nano-sized STO, but, also, for the description of nonlinear auto-oscillating systems of any physical nature.

Nonlinear Auto-Oscillator Theory of Microwave Generation by Spin-Polarized Current

Introductory Fourier transform spectroscopy

Within the field of spectral imaging, the vast majority of instruments used are scanning devices. Recently, several snapshot spectral imaging systems have become commercially available, providing new functionality for users and opening up the field to a wide array of new applications. A comprehensive survey of the available snapshot technologies is provided, and an attempt has been made to show how the new capabilities of snapshot approaches can be fully utilized.

https://www.spiedigitallibrary.org/journals/optical-engineering/volume-52/issue-9/090901/Review-of-snapshot-spectral-imaging-technologies/10.1117/1.OE.52.9.090901.pdf

Review of snapshot spectral imaging technologies

Mobile robot terrain perception is needed to provide local terrain data to assess route trafficability. Important terrain features include positive obstacles (e.g., rocks, walls and posts), negative obstacles (e.g., down-steps, gaps and holes), step slopes (lateral and longitudinal), and broken, rough or porous obstacles (e.g., brush and rubble). Terrain perception is needed to locate these terrain features and assess their geometric properties. Stereo vision (stereoscopy) is a widely-used technique for mobile robot terrain perception. Stereo vision is effective for locating positive obstacles. It has not proven effective at locating and assessing negative features, slope in regions with relatively uniform in appearance, or highly textured features. This paper explores an approach to enhance and complement stereo vision terrain perception by using simple stereo lighting (photometric stereo). Shadows created by vertical- offset lighting are an effective cue to locate negative terrain features. Horizontal-offset illumination enhances cue features in the scene for stereo vision processing. Stereo illumination creates depth cues that can not be exploited by traditional horizontal-offset stereo vision systems, but can be exploited by trinocular or vertically- offset stereo cameras. Multiple light sources and cameras enable shape-from-shading (photoclinometry) methods to overcome traditional limitations to generating 3D range and slope maps for natural terrain. Issues in applying these methods for broken, rough and porous obstacles are identified, but not examined in detail.

Preliminary investigation into the use of stereo illumination to enhance mobile robot terrain perception

Wavelet transforms are currently being used for a number of applications such as cue feature and noise extraction from images and acoustic signals. This work describes and applies an algorithm that uses wavelets for finding the clutter in infrared (IR) and visual images. Once the clutter is found, the probability of detection (Pd) is calculated. Tidhar's and Rotman's probability of edge metric (POE) is extended to encompass the wavelet methodology for multiscale clutter metrics in IR images. © 1996 Society of Photo-Optical Instrumentation Engineers.

Wavelet transforms of cluttered images and their application to computing the probability of detection

Unmanned Systems Technology XI

: By using M.G. Bekker's equations for vehicle mobility, we have developed an analytical tool that is used for the evaluation of off-road vehicles. This allows for the departure from historical empirical models by developing universal static and transient equations that fit for all vehicles. One of our primary purposes is to develop a model for the evaluation of different mobility platforms of small robotic vehicles. These vehicles are evaluated on conditions of tractive force, tractive effort, drawbar-pull, sinkage, safe weight pressures, ground pressures, and slippage. This paper attempts to compare the mobility characteristics of wheeled vs. track vehicles for different size, weight, and terrain based off of aforementioned conditions. BDTM was originally established to give a first pass general evaluation of robotic vehicle mobility performance for different types of running gear configurations. It is a simple, linear one-degree of freedom (1-DOF) model that has been created in a spreadsheet format. The model assumes that the soil is homogenous and the loading effects are linear. The model can simulate both tracked and wheeled vehicles. These vehicles are evaluated using tractive force, tractive effort, soil sinkage, drawbar pull, and tractive coefficients (DP/W).

/pdf/evaluating-vehicle-mobility-using-bekker-s-equations-3vsakgowud.pdf

Evaluating Vehicle Mobility Using Bekker's Equations

Bekker's Derived Terramechanics Model (BDTM) is an analytical tool for evaluating vehicle off-road mobility. BDTM has been developed using Bekker's equations for vehicle soil interactions. He developed the bevameter technique to measure mechanical strength characteristics for many soil and snow conditions. BDTM is in a spreadsheet format, and its primary purpose is to compare mobility characteristics for robotic track and wheeled vehicles under different terrain conditions. Bekker's model is a simple, linear one degree-of-freedom (1-DOF) model, which assumes that in a perfectly cohesive soil (i.e. clay), soil thrust is only a function of contact surfacearea. The model also assumes that for a perfection cohesionless or frictional soil (i.e. dry sand), soil thrust is a function of vehicular weight. This paper attempts to compare the mobility characteristics of wheeled vs. track vehicles for different size, weight and terrain conditions.

Grant R. Gerhart

Papers

Preliminary investigation into the use of stereo illumination to enhance mobile robot terrain perception

Wavelet transforms of cluttered images and their application to computing the probability of detection

Unmanned Systems Technology XI

Evaluating Vehicle Mobility Using Bekker's Equations

Off-Road Vehicle Locomotion Using Bekker’s Model.