Specification and enforcement of flexible security policy for active cooperation

Interoperation and services sharing among different systems are becoming new paradigms for enterprise collaboration. To keep ahead in strong competition environments, an enterprise should provide flexible and comprehensive services to partners and support active collaborations with partners and customers. Achieving such goals requires enterprises to specify and enforce flexible security policies for their information systems. Although the area of access control has been widely investigated, current approaches still do not support flexible security policies able to account for different weighs that typically characterize the various attributes of the requesting parties and transactions and reflect the access control criteria that are relevant for the enterprise. In this paper we propose a novel approach that addresses such flexibility requirements while at the same time reducing the complexity of security management. To support flexible policy specification, we define the notion of restraint rules for authorization management processes and introduce the concept of impact weight for the conditions in these restraint rules. We also introduce a new data structure for the encoding of the condition tree as well as the corresponding algorithm for efficiently evaluating conditions. Furthermore, we present a system architecture that implements above approach and supports interoperation among heterogeneous platforms

Design Evaluation of Self-Learning Line Following Robot

The development of a line-following robot is, for many, a building block into further applications of robotics. One of the main focuses is pathfinding without prior knowledge of the route or environment. In the case of a simple line- following robots, the task is limited, allowing for much room in experimenting with different approaches. In this paper, we present our dual-control line following robot - ATR LineBot. The first control mechanism uses two series of line sensors in combination with a custom PID controller for speed optimization. The additional sensors allows for sophisticated planning and correction to be done. Our second control mechanism utilizes a double layered neural network for remote learning of optimal speed. Utilizing the output of line detection sensors and motor encoder as feature vectors, the neural network can optimize control. ATR LineBot, the dual-control line following robot, uses both PID controlling and reinforcement learning for successful navigation and serves as a platform for improving the traditional building block of robotics. Thus, we present a smart line following robot comprising a robust design, which is compatible with any circumstance as well as efficient in achieving its ideal speed to perceive its goal

Design of a Low-Cost, Open Source, and Open Hardware Educational Drone

In today’s technology-driven world, innovation is high-paced and the importance of learning the skills surrounding emerging technologies has become crucial. One of such emerging technologies are drones, and the need for drone education has become apparent. However, current educational drones are inaccessible to many individuals, and instead are aimed at collegiate education. Furthermore, drones are hazardous, difficult to manipulate, and costly. To address these issues, in this paper we present the ATR-Drone Project. We have developed a safe mechanical platform, composed of 3D printed components and a simple round metal rod that serves to adjust the balance of the drone during stationary flight trials. The drone utilizes an Arduino as its on-board computing platform along with an open- source custom circuit board we have developed. This provides beginner programmers and hobbyists with the opportunity to easily learn about embedded system fundamentals and the mechanics of aerial vehicles. Overall, the ATR-Drone Project is aimed at lowering the educational barrier-to-entry and opening the doors to any individual who wish to learn about drones through an efficient, easy-to-use, and affordable platform