Remote centre-of-motion control algorithms of 6-RRCRR parallel robot assisted surgery system (PRAMiSS)

In this paper a 6-RRCRR parallel robot assisted minimally invasive surgery/microsurgery system (PRAMiSS) is introduced. Remote centre-of-motion (RCM) control algorithms of PRAMiSS suitable for minimally invasive surgery and microsurgery are also presented. The programmable RCM approach is implemented in order to achieve manipulation under the constraint of moving through the fixed penetration point. Having minimised the displacements of the mobile platform of the parallel micropositioning robot, the algorithms also apply orientation constraint to the instrument and prevent the tool tip to orient due to the robot movements during the manipulation. Experimental results are provided to verify accuracy and effectiveness of the proposed RCM control algorithms for minimally invasive surgery.

Delay control and parallel admission algorithms for real-time anycast flow

An anycast flow is a flow that can be connected to any one of the members in a group of designated (replicated) servers (called anycast group). In this paper, we derive a set of formulas for calculating the end-to-end delay bound for the anycast flows and present novel admission control algorithms for anycast flows with real-time constraints. Given such an anycast group, our algorithms can effectively select the paths for anycast flows' admission and connection based on the least end-to-end delay bounds evaluated. We also present a parallel admission control algorithm that can effectively calculate the available paths with a short delay bound for different destinations in the anycast group so that a best path with the shortest delay bound can be chosen.

A new distributed power control formula in CDMA mobile networks

The paper presents a new fully distributed uplink power control method for CDMA systems. The power control algorithm calculates explicitly and assigns directly the desired mobile transmit powers achieving both maximum Carrier-to-Interference Ratio at the base station and minimum mobile energy consumption. Compared with the commonly known iterative power control algorithms, the direct assignment method is easier to implement and more power efficient.

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.

Simulation of underwater robots using MS Robot Studio©

One stage in designing the control for underwater robot swarms is to confirm the control algorithms via simulation. To perform the simulation Microsoftpsilas Robotic Studiocopy was chosen. The problem with this simulator and others like it is that it is set up for land-based robots only. This paper explores one possible way to get around this limitation. This solution cannot only work for underwater vehicles but aerial vehicles as well.

Motion analysis of a robotic assisted surgery and microsurgery system - experimental verification

Motion analysis of a parallel robot assisted minimally invasive surgery/microsurgery system (PRAMiSS) and the control structures enabling it to achieve milli/micromanipulations under the constraint of moving through a fixed penetration point or so-called remote centre-of-motion (RCM) are presented in this article. Two control algorithms are proposed suitable for minimally invasive surgery (MIS) with submillimeter accuracy and for minimally invasive micro-surgery (MIMS) with submicrometer accuracy. The RCM constraint is performed without having any mechanical constraint. Control algorithms also apply orientation constraint preventing the tip to orient relative to the soft tissues due to the robot movements. Experiments were conducted to verify accuracy and effectiveness of the proposed control algorithms for MIS and MIMS operations. The experimental results demonstrate accuracy and performance of the proposed position control algorithms.

The rough sets feature selection for trees recognition in color aerial images using genetic algorithms

Selecting a set of features which is optimal for a given task is the problem which plays an important role in a wide variety of contexts including pattern recognition, images understanding and machine learning. The concept of reduction of the decision table based on the rough set is very useful for feature selection. In this paper, a genetic algorithm based approach is presented to search the relative reduct decision table of the rough set. This approach has the ability to accommodate multiple criteria such as accuracy and cost of classification into the feature selection process and finds the effective feature subset for texture classification . On the basis of the effective feature subset selected, this paper presents a method to extract the objects which are higher than their surroundings, such as trees or forest, in the color aerial images. The experiments results show that the feature subset selected and the method of the object extraction presented in this paper are practical and effective.

Location based power control for mobile devices in a cellular network

This paper provides location estimation based power control strategy for cellular radio systems via a location based interference management scheme. Our approach considers the carrier-to-interference as dependent on the transmitter and receiver separation distance and therefore an accurate estimation of the precise locations can provide the power critical mobile user to control the transition power accordingly. In this fully
distributed algorithms, we propose using a Robust Extended Kalman Filter (REKF) to derive an estimate of the mobile user’s closest mobile base station from the user’s location, heading and altitude. Our analysis demonstrates that this algorithm can successfully track the mobile users with less system complexity, as it requires measurements from only one or two closest mobile base stations and hence enable the user to transmit at the rate that is sufficient for the interference management. Our power control
algorithms based on this estimation converges to the desired power trajectory. Further, the technique is robust against system uncertainties caused by the inherent deterministic nature of the mobility model. Through simulation, we show the accuracy of our prediction algorithm and the simplicity of its implementation.

Spam filtering using ML algorithms

Spam is commonly defined as unsolicited email messages, and the goal of spam categorization is to distinguish between spam and legitimate email messages. Spam used to be considered a mere nuisance, but due to the abundant amounts of spam being sent today, it has progressed from being a nuisance to becoming a major problem. Spam filtering is able to control the problem in a variety of ways. Many researches in spam filtering has been centred on the more sophisticated classifier-related issues. Currently,  machine learning for spam classification is an important research issue at present. Support Vector Machines (SVMs) are a new learning method and achieve substantial improvements over the currently preferred methods, and behave robustly whilst tackling a variety of different learning tasks. Due to its high dimensional input, fewer irrelevant features and high accuracy, the  SVMs are more important to researchers for categorizing spam. This paper explores and identifies the use of different learning algorithms for classifying spam and legitimate messages from e-mail. A comparative analysis among the filtering techniques has also been presented in this paper.

Malware variant detection using similarity search over sets of control flow graphs

Static detection of polymorphic malware variants plays an important role to improve system security. Control flow has shown to be an effective characteristic that represents polymorphic malware instances. In our research, we propose a similarity search of malware using novel distance metrics of malware signatures. We describe a malware signature by the set of control flow graphs the malware contains. We propose two approaches and use the first to perform pre-filtering. Firstly, we use a distance metric based on the distance between feature vectors. The feature vector is a decomposition of the set of graphs into either fixed size k-sub graphs, or q-gram strings of the high-level source after decompilation. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flow graphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms.

An illumination invariant face recognition system for access control using video

Illumination and pose invariance are the most challenging aspects of face recognition. In this paper we describe a fully automatic face recognition system that uses video information to achieve illumination and pose robustness. In the proposed method, highly nonlinear manifolds of face motion are approximated using three Gaussian pose clusters. Pose robustness is achieved by comparing the corresponding pose clusters and probabilistically combining the results to derive a measure of similarity between two manifolds. Illumination is normalized on a per-pose basis. Region-based gamma intensity correction is used to correct for coarse illumination changes, while further refinement is achieved by combining a learnt linear manifold of illumination variation with constraints on face pattern distribution, derived from video. Comparative experimental evaluation is presented and the proposed method is shown to greatly outperform state-of-the-art algorithms. Consistent recognition rates of 94-100% are achieved across dramatic changes in illumination.

Control flow-based malware variant detection

Static detection of malware variants plays an important role in system security and control flow has been shown as an effective characteristic that represents polymorphic malware. In our research, we propose a similarity search of malware to detect these variants using novel distance metrics. We describe a malware signature by the set of control flowgraphs the malware contains. We use a distance metric based on the distance between feature vectors of string-based signatures. The feature vector is a decomposition of the set of graphs into either fixed size k-subgraphs, or q-gram strings of the high-level source after decompilation. We use this distance metric to perform pre-filtering. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flowgraphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms. © 2013 IEEE.

Prediction interval-based modelling and control of nonlinear processes

 Novel computational intelligence-based methods have been investigated to quantify uncertainties prevalent in the operation of chemical plants. A new family of predication interval-based controlling algorithms is proposed and successfully applied to chemical reactors in order to minimise energy consumption and operational cost.