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

Vehicle-borne smuggling is widespread because of the availability, flexibility and capacity of the cars and trucks. Inspecting vehicles at border crossings and checkpoints are key security elements. At the present time, most vehicle security inspections at home and abroad are conducted manually. Remotely operated vehicle inspection robots could be integrated into the operating procedures to improve throughput while reducing the workload burden on security personnel. The robotic inspection must be effective at detecting contraband and efficient at clearing the "clean" vehicles that make up the bulk of the traffic stream, while limiting the workload burden on the operators.
In this paper, we present a systems engineering approach to robotic vehicle inspection. We review the tactics, techniques and procedures to interdict contraband. We present an operational concept for robotic vehicle inspection within this framework, and identify needed capabilities. We review the technologies currently available to meet these needs. Finally, we summarize the immediate potential and R&D challenges for effective contraband detection robots.

Robotic inspection for vehicle-borne contraband

A great deal of effort has been directed toward acoustic applications in the marine environment However, the earliest acoustic work was in the above ground environment and that remains an area of interest today The purpose of this chapter is to discuss some aspects of this area that are relevant to vehicle detection, location, classification and identification It is not intended to be exhaustive in nature but to emphasize fundamental effects, present relevant examples, and leave the reader with a broad view of the issues

Application of Acoustics in the Land Environment

Detection and tracking of moving targets in outdoor scenes has a number of important applications such as video surveillance, intelligent-highway systems, battlefield surveillance, etc. Target detection is generally accomplished by discrimination between target and background using locally computed images features such as texture, edges, etc. However, most effort has been in the development of computational algorithms using signal analysis methods[1,2,3]. As a result, these algorithms only work in narrowly defined environments, and usually do not give good predictive results when they are applied to highly resolved targets in complex background scenes[2,3]. Dynamic scene analysis involves image matching, moving object tracking, recovering object structure from motion, object motion analysis, etc. [4]. Many existing vehicle tracking systems used single objects moving in indoor with controlled lighting conditions. The most significant vehicle tracking systems that have been tested under complex outdoor conditions are described in [5]. This is still an active research area and many research issues remain open for investigation[4,5,6].

Dyta: An Intelligent System for Dynamic Target Analysis

The computation of optical flow can play an important role in background characterization and target detectability algorithms since motion is an important cue feature for target detection. A multiresolution gradient-based motion estimation algorithm is implemented based on Horn's approach, where the optical flow field is iteratively determined at each level of the multiresolution pyramid. Different masks and kernels are tested, and an adaptive error minimization approach is taken to deal with areas in the image that violate the optical flow constraint equation.

Multiresolution-based motion estimation

Some interest has recently been shown in determining the reliability of unmanned ground vehicles network. Based
on the predicted reliability, a commander can take appropriate action in the battle field operation for unmanned
ground vehicles network. The reliability should include coordination and collaboration of a number of different
unmanned ground vehicles. Some approaches for determining the reliability of unmanned vehicles have been
discussed in the literatures. In this paper, we propose a new algorithm by which the reliability of unmanned ground
vehicles network is predicted using fuzzy approach. The approach is different from statistical approaches discussed
in previous papers. The algorithm suggested here is based on all simple paths obtained between the terminal nodes
of the network in question or the cutset expression of the terminal nodes. Each variable in the Boolean expression
obtained from simple path method or from the cutset method will be subjectively assigned a membership grade by
an expert in the field. Fuzzy union and fuzzy intersection operations are used to predict the reliability of the network
in question. New theorems have been developed to determine the disjoint expression using fuzzy logic. The
comparison of this new approach is given with the existing approaches. Further, assuming node reliability and
branch reliability one can also predict system reliability of the unmanned ground vehicles network using fuzzy logic.

Grant R. Gerhart

Papers

Robotic inspection for vehicle-borne contraband

Application of Acoustics in the Land Environment

Dyta: An Intelligent System for Dynamic Target Analysis

Multiresolution-based motion estimation

A new approach for determining reliability of unmanned vehicles network using fuzzy logic