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

: This paper presents a simulation and comparison of two different infrared (IR) imaging systems in terms of their use in automotive collision avoidance and vision enhancement applications. The first half of this study concerns the simulations of a "cooled" shortwave focal plane array infrared imaging system, arid an "uncooled" focal plane array infrared imaging system. That is done using the United States Army's Tank-Automotive Research Development and Engineering Center's (TARDEC) Thermal Image Model - (TTlM). Visual images of automobiles as seen through a forward looking infrared sensor are generated, by using TrIM, under a variety of viewing range, and rain conditions. The second half of the study focuses on a comparison between the two simulated sensors. This comparison is undertaken from the standpoint of the ability of a human observer to detect potential (collision) targets, when looking through the two different sensors A measure of the target's detectability is derived for each sensor by using the TARDEC's Visual Model (TVM). The authors found the uncooled pyroelectric FPA to give excellent imagery and, combined with the advantages of the 7.5-13.5 band in the atmosphere and the higher blackbody existance in the 7.5-13.5 band, the 7.5-13.5 uncooled sensor is therefore the better choice for imaging through numerous atmospheric conditions compared to the 3.4-5.5 cooled sensor.

Simulation and Comparison of Infra-red Sensors for Automotive Applications

There is an increasing interest in the army of small unmanned robots taking part in defense operations. It is considered
important to predict the reliability of the group of robots taking part in different operations. A group of robots have both
coordination and collaboration. The robot operations are considered as a network graph whose system reliability can be
determined with the help of different techniques. Once a specified reliability is achieved the commander controlling the
operation can take appropriate action. This paper gives a simulation which can determine the system reliability of the
robotic systems having collaboration and coordination. The procedure developed is based on binary decision diagrams to
obtain a disjoint Boolean expression. The procedure is applicable for any number of nodes and the branches. For
illustration purposes reliability of simple circuits like series network, parallel network, series-parallel and non-series
parallel network are illustrated. It is hoped that more work in this area will lead to the development of algorithms which
will be ultimately used for a real time environment.

Simulation of an algorithm for determining the reliability of unmanned ground vehicle networks

Unmanned Systems Technology X

Development of software for autonomous ground vehicles has been a mission critical issue for army. For the last two decades, different definitions of software complexity have been proposed. However, no definition has been found to be highly satisfactory. So in this report, a new definition of software complexity based on the rank of a matrix of a finite sequential machine has been proposed. Software has been developed to determine the complexity, so that it could be used in the early stages of software development from the data flow architecture to save cost and development efforts. We have shown the examples of data flow architecture and then complexity calculation based on our software. It is hoped that the results in this paper will help software development to address reliability and complexity issues of autonomous ground vehicle in a better way to assist research in defense and security for such missions.

Finite state modeling of mobile robots for complexity determination

A number of research workers have applied intelligent approaches for robotic applications. In the recent literature there is an increasing role of fuzzy and Neuro fuzzy approaches for unmanned vehicles. Both these approaches are based on intelligent rules. However for these applications the rules become very large and so computational time is very high. It is important to explore the approaches so as to reduce the computation time. In this paper a combination of factor analysis and clustering approaches is suggested so as to reduce the number of rules. The factor analysis can be used to reduce the number of parameters while clustering approach can be used to reduce the number of observations. Based on this methodology a new algorithm is developed which reduces the original parameters and observations into a set of new data. An algorithm is proposed and applied on a real robotic data available in a previous paper. Some of the applications for future work are proposed.

Grant R. Gerhart

Papers

Simulation and Comparison of Infra-red Sensors for Automotive Applications

Simulation of an algorithm for determining the reliability of unmanned ground vehicle networks

Unmanned Systems Technology X

Finite state modeling of mobile robots for complexity determination

On a new approach to reduction of data for ANFIS application to unmanned robotic vehicles