Collaborative Verification-Driven Engineering of Hybrid Systems

Hybrid systems with both discrete and continuous dynamics are an important model for real-world cyber-physical systems. The key challenge is to ensure their correct functioning w.r.t. safety requirements. Promising techniques to ensure safety seem to be model-driven engineering to develop hybrid systems in a well-defined and traceable manner, and formal verification to prove their correctness. Their combination forms the vision of verification-driven engineering. Often, hybrid systems are rather complex in that they require expertise from many domains (e.g., robotics, control systems, computer science, software engineering, and mechanical engineering). Moreover, despite the remarkable progress in automating formal verification of hybrid systems, the construction of proofs of complex systems often requires nontrivial human guidance, since hybrid systems verification tools solve undecidable problems. It is, thus, not uncommon for development and verification teams to consist of many players with diverse expertise. This paper introduces a verification-driven engineering toolset that extends our previous work on hybrid and arithmetic verification with tools for (i) graphical (UML) and textual modeling of hybrid systems, (ii) exchanging and comparing models and proofs, and (iii) managing verification tasks. This toolset makes it easier to tackle large-scale verification tasks

A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e. deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision - convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.Comment: Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm

Optical modeling of agricultural fields and rough-textured rock and mineral surfaces

Review was made of past models for describing the reflectance and/or emittance properties of agricultural/forestry and geological targets in an effort to select the best theoretical models. An extension of the six parameter Allen-Gayle-Richardson model was chosen as the agricultural plant canopy model. The model is used to predict the bidirectional reflectance of a field crop from known laboratory spectra of crop components and approximate plant geometry. The selected geological model is based on Mie theory and radiative transfer equations, and will assess the effect of textural variations of the spectral emittance of natural rock surfaces

The effect of rotor design on the fluid dynamics of helicopter brownout

Helicopters operating close to the ground in dusty environments tend to generate large clouds of dust in the surrounding air. These clouds can obscure the pilot's view of the ground and lead to a dangerous condition known as brownout. Given the intimate relationship between the induced flow feld around the rotor and the process through which the particulate matter becomes airborne and is subsequently transported, it has been speculated that the design of its rotor may influence the shape and size of the dust clouds that are produced by any particular type of helicopter. This paper presents a study of the influence of two key geometric properties of the rotor on the development of these dust clouds. A particle transport model is coupled to Brown's Vorticity Transport Model to represent the dynamics of the particulate-air system surrounding a generic helicopter rotor under various flight conditions. The number of blades on the rotor is altered, whilst keeping the solidity constant, thus altering the distribution of vorticity that is released onto the ground. In addition, the twist of the blades is varied in order to investigate the effect of the resultant changes in the distribution of induced downwash on the evolution of the dust cloud. The study suggests that, in general, the larger the number of blades, and the higher the blade twist, the less dense the dust clouds that are produced under brownout conditions. It appears thus that the characteristics of the dust clouds are indeed sensitive to the geometry of the rotor and hence that careful aerodynamic design may allow the severity of brownout to be ameliorated