Geometric formations in swarm aggregation: an artificial formation force based approach

Cooperative control of multiple mobile robots is an attractive and challenging problem which has drawn considerable attention in the recent past. This paper introduces a scalable decentralized control algorithm to navigate a group of mobile robots (swarm) into a predefined shape in 2D space. The proposed architecture uses artificial forces to control mobile agents into the shape and spread them inside the shape while avoiding inter-member collisions. The theoretical analysis of the swarm behavior describes the motion of the complete swarm and individual members in relevant situations. We use computer simulated case studies to verify the theoretical assertions and to demonstrate the robustness of the swarm under external disturbances such as death of agents, change of shape etc.

Identification of moving obstacles with pyramidal Lucas Kanade optical flow and k means clustering

This paper describes the methodology for identifying moving obstacles by obtaining a reliable and a sparse optical flow from image sequences. Given a sequence of images, basically we can detect two-types of on road vehicles, vehicles traveling in the opposite direction and vehicles traveling in the same direction. For both types, distinct feature points can be detected by Shi and Tomasi corner detector algorithm. Then pyramidal Lucas Kanade method for optical flow calculation is used to match the sparse feature set of one frame on the consecutive frame. By applying k means clustering on four component feature vector, which are to be the coordinates of the feature point and the two components of the optical flow, we can easily calculate the centroids of the clusters and the objects can be easily tracked. The vehicles traveling in the opposite direction produce a diverging vector field, while vehicles traveling in the same direction produce a converging vector field

An authorization policy management framework for dynamic medical data sharing

In this paper, we propose a novel feature reduction approach to group words hierarchically into clusters which can then be used as new features for document classification. Initially, each word constitutes a cluster. We calculate the mutual confidence between any two different words. The pair of clusters containing the two words with the highest mutual confidence are combined into a new cluster. This process of merging is iterated until all the mutual confidences between the un-processed pair of words are smaller than a predefined threshold or only one cluster exists. In this way, a hierarchy of word clusters is obtained. The user can decide the clusters, from a certain level, to be used as new features for document classification. Experimental results have shown that our method can perform better than other methods.

A lower bound on effective performance testing for digital forensic tools

The increasing complexity and number of digital forensic tasks required in criminal investigations demand the development of an effective and efficient testing methodology, enabling tools of similar functionalities to be compared based on their performance. Assuming that the tool tester is familiar with the underlying testing platform and has the ability to use the tools correctly, we provide a numerical solution for the lower bound on the number of testing cases needed to determine comparative capabilities of any set of digital forensic tools. We also present a case study on the performance testing of password cracking tools, which allows us to confirm that the lower bound on the number of testing runs needed is closely related to the row size of certain orthogonal arrays. We show how to reduce the number of test runs by using knowledge of the underlying system

Stability of 2-D characteristic polynomials

This paper derives some new conditions for the bivariate characteristic polynomial of an uncertain matrix to be very strict Hurwitz. The uncertainties are assumed of the structured and unstructured type. By using the two-dimensional (2-D) inverse Laplace transform, the bounds on the uncertainties are derived which will ensure that the bivariate characteristic polynomial to be very strict Hurwitz. Two numerical examples are given to illustrate the results.

Secure mobile agent-based e-negotiation for on-line trading

Electronic negotiation (e-negotiation) is a major activity in e-Commerce applications. Agent-based e-negotiation has recently received increasing attention. However, agent-based electronic negotiation suffers from a number of security attacks. In this paper, we present a mobile agent-based e-commerce framework. We also propose a security protocol that protects the information exchanged between the mobile agents during e- negotiations. We reason the correctness of the proposed security protocol in the presence of various security threats. The reasoning shows that the protocol maintains privacy, non- repudiation, authenticity, anonymity, and strong integrity of exchanged information.

An authentication framework for e-health systems

The high-level sensitivity of medical information mandates stronger authentication and authorization mechanisms to be used in e-Health systems. This paper describes the design and implementation of certificate-based e-Health authentication and authorization architecture. This architecture was developed to authenticate e-Health professionals accessing shared clinical data among a set of affiliated health institutions based on peer-to- peer networks. The architecture had to accommodate specific medical data sharing and handling requirements, namely the security of professionals' credentials.

Email categorization using multi-stage classification technique

This paper presents an innovative email categorization using a serialized multi-stage classification ensembles technique. Many approaches are used in practice for email categorization to control the menace of spam emails in different ways. Content-based email categorization employs filtering techniques using classification algorithms to learn to predict spam e-mails given a corpus of training e-mails. This process achieves a substantial performance with some amount of FP tradeoffs. It has been studied and investigated with different classification algorithms and found that the outputs of the classifiers vary from one classifier to another with same email corpora. In this paper we have proposed a multi-stage classification technique using different popular learning algorithms with an analyser which reduces the FP (false positive) problems substantially and increases classification accuracy compared to similar existing techniques.

Fuzzy haptic augmentation for telerobotic stair climbing

Teleoperated robotic systems provide a valuable solution for the exploration of hazardous environments. The ability to explore dangerous environments from the safety of a remote location represents an important progression towards the preservation of human safety in the inevitable response to such a threat. While the benefits of removing physical human presence are clear, challenges associated with remote operation of a robotic system need to be addressed. Removing direct human presence from the robot's operating environment introduces telepresence as an important consideration in achieving the desired objective. The introduction of the haptic modality represents one approach towards improving operator performance subject to reduced telepresence. When operating in an urban environment, teleoperative stair climbing is not an uncommon scenario. This work investigates the operation of an articulated track mobile robot designed for ascending stairs under teleoperative control. In order to assist the teleoperator in improved navigational capabilities, a fuzzy expert system is utilised to provide the teleoperator with intelligent haptic augmentation with the aim of improving task performance.

A parallel downloading algorithm for redundant networks

In this paper, we study the downloading mechanism of BitTorrent (or BT), a P2P based popular and convenient parallel downloading software tool, point out some of its limitations, and propose an algorithm to improve its performance. In particular, we address the limitations of BT by using neighbours in P2P networks to resolve the redundant copies problem and to optimise the downloading speed. Our preliminary experiments show that the proposed enhancement algorithm works well.

A resource-search and routing algorithm within PCDN autonomy area

This paper studied a new type of network model; it is formed by the dynamic autonomy area, the structured source servers and the proxy servers. The new network model satisfies the dynamics within the autonomy area, where each node undertakes different tasks according to their different abilities, to ensure that each node has the load ability fit its own; it does not need to exchange information via the central servers, so it can carry out the efficient data transmission and routing search. According to the highly dynamics of the autonomy area, we established dynamic tree structure-proliferation system routing and resource-search algorithms and simulated these algorithms. Test results show the performance of the proposed network model and the algorithms are very stable.

An innovative analyser for email classification based on grey list analysis

In this paper we propose a new technique of email classification based on grey list (GL) analysis of user emails. This technique is based on the analysis of output emails of an integrated model which uses multiple classifiers of statistical learning algorithms. The GL is a list of classifier/(s) output which is/are not considered as true positive (TP) and true negative (TN) but in the middle of them. Many works have been done to filter spam from legitimate emails using classification algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In the case of spam detection the FP problem is unacceptable, sometimes. The proposed technique will provide a list of output emails, called "grey list (GL)", to the analyser for making decisions about the status of these emails. It has been shown that the performance of our proposed technique for email classification is much better compare to existing systems, in order to reducing FP problems and accuracy.

Bilateral haptic teleoperation of an articulated track mobile robot

Teleoperation has been used in many applications, allowing a human operator to remotely control a robotic system in order to perform a particular task. Recently haptic teleoperation has focused mainly on improving performance in remote manipulation tasks, however the haptic approach offers similar advantages for teleoperative control of the motion of a mobile robot. This paper describes a prototype system designed to facilitate haptic teleoperation of an all-terrain, articulated track mobile robot. This system utilizes a multi-modal user interface intended to improve operator immersion, reduce operator overload and improve teleoperative task performance. The system architecture facilitates implementation of an application-specific haptic augmentation algorithm in order to improve operator performance in challenging real-world tasks. The contributions of this work can be categorized as the custom mobile platform, teleoperator interface and haptic augmentation strategy.

OzBot TM - Haptic augmentation of a teleoperated robotic platform for search and rescue operations

A continued increase in computing power, sensor capability, software functionality, immersive interfaces and hardware modularity has given robot designers seemingly endless potential in the area of mobile robotics.  While some mobile robotic system designers are focusing on expensive, full-featured platforms, developers are realising the advantages of emerging technology in providing small, low-cost mobile reconnaissance vehicles as expendable teleoperated robotic systems.  The OzBot^TM mobile reconnaissance platform presents one such system.  The design objectives of the OzBot^TM platform focus on the development of inexpensive, lightweight carry-case sized robots for search and rescue operations, law enforcement scenarios and hazardous environment inspection.  The incorporation of Haptic augmentation provides the teleoperator with improved task immersion for an outdoor search and rescue scenario.  Achieved in cooperation with law enforcement agencies within Australia, this paper discusses the performance of the first four revisions of the OzBot^TM platform.

A haptically enabled low-cost reconnaissance platform for law enforcement

Traditionally, the control system of a modern teleoperated mobile robot consists of one or more two-dimensional joysticks placed on a control interface. While this simplistic interface allows an operator to remotely drive the platform, feedback is limited to visual information supplied by on-board cameras. Significant advances in the field of haptics have the potential to meaningfully enhance situational awareness of a remote robot. The focus of this research is the augmentation of Deakin University's OzBot trade MkIV mobile platform to include haptic control methodologies. Utilising the platform's inertial measurement unit, a remote operator has the ability to gain knowledge of the vehicle's operating performance and terrain while supplying a finer level of control to the drive motors. Our development of a generic multi-platform ActiveX allows the easy implementation of haptic force feedback to many computer based robot controllers. Furthermore, development of communication protocols has progressed with Joint Architecture for Unmanned Systems (JAUS) compliance in mind. The haptic force control algorithms are presented along with results highlighting the benefits of haptic operator feedback on the MklV OzBot trade chassis.