Proposes a framework for simultaneous detection, tracking, and recognition of objects via data fused from multiple sensors. Complex dynamic scenes are represented via the concatenation of simple rigid templates. The variability of the infinity of pose is accommodated via the actions of matrix Lie groups extending the templates to individual instances. The variability of target number and target identity is accommodated via the representation of scenes as unions of templates of varying types, with the associated group transformations of varying dimension. We focus on recognition in the air-to-ground and ground-to-air scenarios. The remote sensing data is organized around both the coarse scale associated with detection as provided by tracking and range radars, along with the fine scale associated with pose and identity supported by high-resolution optical, forward looking infrared and delay-Doppler radar imagers. A Bayesian approach is adopted in which prior distributions on target scenarios are constructed via dynamical models of the targets of interest. These are combined with physics-based sensor models which define conditional likelihoods for the coarse/fine scale sensor data given the underlying scene. Inference via the Bayes posterior is organized around a random sampling algorithm based on jump-diffusion processes. New objects are detected and object identities are recognized through discrete jump moves through parameter space, the algorithm exploring scenes of varying complexity as it proceeds. Between jumps, the scale and rotation group transformations are generated via continuous diffusions in order to smoothly deform templates into individual instances of objects.

Automatic target recognition organized via jump-diffusion algorithms

The number of tasks that nowadays are accomplished by using unmanned aerial vehicles is rising across many civil applications, including agriculture. Thus, this work aims at providing the reader with an overview of the agronomical use of unmanned aerial vehicles. The work starts with a historical analysis of the use of aircrafts in agriculture, as pioneers of their use in modern precision agriculture techniques, currently applied by a high number of users. This survey has been carried out by providing a classification of the vehicles according to their typology and main sensorial and performance features. An extensive review of the most common applications and the advantages of using unmanned aerial vehicles is the core of the work. Finally, a brief summary of the key points of the legislation applicable to civil drones that could affect to agricultural applications is analyzed.

Unmanned Aerial Vehicles in Agriculture: A Survey

Vehicle traffic congestion is reflected as delays while traveling. Traffic congestion has a number of negative effects and is a major problem in today's society. Several techniques have been deployed to deal with this problem. In this paper, we have proposed an innovative approach to deal with the problem of traffic congestion using the characteristics of vehicular ad-hoc networks (VANET). We have used the Adaptive Proportional Integral rate controller, a congestion control algorithm designed for the Internet, to deal with the problem of vehicle traffic congestion in vehicular networks. The adaptive PI rate controller is a rate based controller that employs control theory to manage the problem of data traffic congestion in computer networks. Using simulations we have demonstrated the applicability of the algorithm in the domain of vehicle traffic congestion in a VANET.

Vehicle traffic congestion management in vehicular ad-hoc networks

Traffic system shows a great scope of trade with the environment and is directly connected to it. Manual traffic systems are proving to be insufficient due to rapid urbanization. Central monitoring systems are facing scalability issues as they process increasing amounts of data received from hundreds of traffic cameras. Major traffic problems include congestion, safety, pollution (leading to various health issues) and increased need for mobility. A solution to most of them is the construction of newer and safer highways and additional lanes on existing ones, but it proves to be expensive and often not feasible. Cities are limited by space, and construction cannot keep up with ever-growing demand. Hence, a need for an improved system with a minimal manual interface is persisting. One of such methods is introduced and discussed in this paper; smart traffic governance system here used artificial intelligence to regulate and govern the course of transport and automated administration and implementation to make a difference in face of travel scenarios in urban cities suffering from such major traffic issues.

Optimization of Smart Traffic Governance System Using Artificial Intelligence

An intelligent vehicle highway system collects and provides real-time traffic data to a vehicle traveling on a continental roadway network. A digitized continental roadway network has nodes interconnected by links that define a digitized representation of the continental roadway network. To enable intelligent data collection and navigation over large expanses of the continental network, which would otherwise be computationally onerous, the digitized continental roadway network is partitioned into a plurality of digitized roadway subnetworks. The onboard vehicle navigation device has a processor for executing an application that instantiates a subset of the digitized roadway subnetworks in a vicinity of a current position of the vehicle to collect and provide relevant real-time traffic data to the vehicle in a computationally efficient manner.

Method and system for partitioning a continental roadway network for an intelligent vehicle highway system

Target recognition systems have undergone a variety of changes over the past 20 years. Initial systems exploited signal processing techniques to detect ground-based targets based on one-dimensional signals. Limitations of these systems eventually led to the development of automated target recognizers (ATRs) that processed two-dimensional digital images to detect, classify, and identify targets. Though their performance exceeded that of signal processing systems, ATRs exhibited several deficiencies to which artificial intelligence (Al) offered numerous potential solutions. This paper reviews the evolution of target recognition systems with primary focus on Al applications. Deficiencies of Al approaches to target recognition are presented and complemented by a discussion of a blackboard-based ATR system currently being developed at Georgia Tech.

Knowledge-based target recognition system evolution

The advent of advanced computer architectures for parallel and symbolic processing has evolved technology to the point at which prototype autonomous vehicles are being developed. Control of such devices requires communication between knowledge-based subsystems in charge of the vision, planning, and control aspects necessary to make autonomous systems func-tional in a real-world environment. The performance of autonomous vehicle systems is currently limited by their inability to accurately analyze their surrounding environment. In order to function in dynamic situations, autonomous vehicles must be capable of interpreting terrain on the basis of predetermined mission goals. This paper describes an autonomous airborne-vehicle simulation currently being developed at the Georgia Tech Research Institute. The Autonomous Helicopter System (AHS) is a multimission system consisting of three distinct sections: vision, planning, and control. The vision section provides the local and global scene analyses that are symbolically represented and passed to the planning section as the initial route-planning constraints. The planning section generates a task-dependent path for the vehicle to traverse that assures maximum mission system successes as well as survivability. The control section validates the path and either executes the given route or feeds back to previous sections in order to resolve conflicts.

Knowledge-Based Approach Toward Developing An Autonomous Helicopter System

Diagnostic expert systems is an area of growing interest in the application of expert system technology. Diagnosis is a step-by-step process that attempts to ascertain the internal characteristics of a physical system. The first generation of knowledge-based systems for hardware diagnosis are based primarily on the largely empirically derived knowledge of human experts. A number of diagnostic expert systems dealing with electronic and computer hardware have been developed over the last several years. The majority of these systems have utilized a variety of diagnostic techniques and concepts to solve a specific hardware application. This paper surveys four of the most successful diagnostic expert systems in the areas of computers and electronic hardware and analyzes each in a comparative manner. Several additional systems are summarized to provide the reader with a relatively extensive overview of existing research and development activities in the area of diagnostic expert systems.

A Survey Of Diagnostic Expert Systems

Existing strategies for the identification of objects in a scene are based upon classical pattern recognition approaches. The basic concept involved centers around the extraction of a set of statistical features for each object detected in a scene, followed by the application of a classifier which attempts to derive the decision boundaries that separate these objects into classes. As statistical features are quite sensitive to noise, this approach has led to problems due to the inability of classifiers to identify accurate feature set separation in less than ideal conditions. A global approach utilizing the contextual information in a scene currently discarded offers the most promise in overcoming the short-comings of current object classification methods.

A Model Driven System for Contextual Scene Analysis

This paper describes an autonomous airborne vehicle being developed at the Georgia Tech Engineering Experiment Station. The Autonomous Helicopter System (AHS) is a multi-mission system consisting of three distinct sections: vision, planning and control. Vision provides the local and global scene analysis which is symbolically represented and passed to planning as the initial route planning constraints. Planning generates a task dependent path for the vehicle to traverse which assures maximum mission system success as well as safety. Control validates the path and either executes the given route or feeds back to previous sections in order to resolve conflicts.

John F. Gilmore

Papers

Knowledge-based target recognition system evolution

Knowledge-Based Approach Toward Developing An Autonomous Helicopter System

A Survey Of Diagnostic Expert Systems

A Model Driven System for Contextual Scene Analysis

The Autonomous Helicopter System