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 research is part of a broader effort to develop a supervisory control system for small robot navigation. Previous research and development focused on a "one-touch, point-and-go" navigation control system using visual homing. In the current research, we have begun to investigate visual tracking methods to extend supervisory control to tasks involving tracking and pursuit of a moving object. Ground-to-ground tracking of arbitrary targets in natural and damaged environments is challenging. Automatic tracking is expected to fail due to line-of-sight obstruction, lighting gradients, rapid changes in perspective and orientation, etc. In supervisory control, the automatic tracker needs able to alert the operator when it is at risk of losing track or when it may have already lost track, and do so with a low false alarm rate. The focus of the current research is on detecting tracking failure during pursuit. We are attempting to develop approaches to detecting failure that can integrate different low-level tracking algorithms. In this paper, we demonstrate stereo vision methods for pursuit tracking and examine several indicators of track loss in field experiments with a variety of moving targets in natural environment.

Visual tracking for mobile robot pursuit

TARDEC has been developing payloads for small robots as part of its unmanned ground vehicle (UGV)
development programs. These platforms typically weigh less than 100 lbs and are used for various physical security
and force protection applications. This paper will address a number of technical issues including platform mobility,
payload positioning, sensor configuration and operational tradeoffs. TARDEC has developed a number of robots
with different mobility mechanisms including track, wheel and hybrid track/wheel running gear configurations. An
extensive discussion will focus upon omni-directional vehicle (ODV) platforms with enhanced intrinsic mobility for
positioning sensor payloads. This paper also discusses tradeoffs between intrinsic platform mobility and articulated
arm complexity for end point positioning of modular sensor packages.

Sensor Deployment on Unmanned Ground Vehicles

: Support vector machines are classification algorithms based on quadratic programming that have been found to give excellent classification results on problems such as discriminating targets versus backgrounds. A key capability of these algorithms is that they need not require a preprocessing step to find feature vectors, yet preprocessing is still typically used. Preprocessing is still an important step in the classification process. We discuss what type of preprocessing steps are useful in improving the classifications results of support vector machines. We first give a short introduction to support vector machines. Then the algorithm is applied to the MSTAR radar data set. Several methods to preprocess the data are used before being sent to the support vector machine. The effect on the classification rate of the algorithm is then determined.

http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA457937&Location=U2&doc=GetTRDoc.pdf

Classification Consequences of Preprocessing Radar Data

Support vector machines are classification algorithms based on quadratic programming that have been found to give excellent classification results on problems such as discriminating targets form backgrounds. A key capability of these algorithms is that they do not require a preprocessing step to determine feature vectors, yet preprocessing is still an important step in the classification process. We discuss the effects of preprocessing feature data on the support vectors and the classification results of support vector machines. We first give a short introduction to support vector machines. Several methods to preprocess the data before being sent to the support vector machine are discussed. Then the algorithm is applied a set of second- order stochastic textures defined by their covariance structure. The effect on the classification rate is then determined.

Consequences of preprocessing feature data for support vector machines

This paper contains the results of a joint investigation by the US Army Engineer Topographic Laboratories (ETL) and the US Army Tank-Automotive Research and Development Command (TARADCOM). The main emphasis is to describe investigations using Direct Electronic Fourier Transform (DEFT) technology and the DEFT method to generate Fourier transforms for surveillance and countersurveillance. These investigations include quantitative results which are obtained with an early device and a comparison of these results with subjective human judgments. A computer simulation of DEFT spectra is performed for the purpose of bandwith ex-tension and signal-to-noise improvement. DEFT devices are a recent advance in acousto-optic technology. Early devices generate a real-time, one-dimensional Fourier transform of a two-dimensional image. This Fourier transform is a limited bandwidth radio frequency spectrum representing the image. This paper contains a very brief description of DEFT technology and appropriate references on the development of the DEFT device.© (1980) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Grant R. Gerhart

Papers

Visual tracking for mobile robot pursuit

Sensor Deployment on Unmanned Ground Vehicles

Classification Consequences of Preprocessing Radar Data

Consequences of preprocessing feature data for support vector machines

Direct Electronic Fourier Transform (DEFT) Spectra For Surveillance And Countersurveillance