Characterization of indoor wireless channel at 5 GHz U-NII bands

The paper reports on the experiments undertaken at the University of Wollongong to characterise fading profiles and delay parameters of an indoor wireless channels at 5 GHz U-NII bands. The measurements were undertaken at different locations around the campus with results recorded for a post-processing to calculate the Rician K-factor, the level crossing rate and the average fade duration as well as mean excess delay, rms delay spread, and the coherence bandwidth of the channel. The presented measurement results can be useful in developing a Markov chain based model of the transport channel for IEEE802.11a or HYPRLAN-2 networks. The results also indicate scenarios where the coherence bandwidth of the channel is smaller than the width of the sub-carrier OFDM channels in either of the mentioned systems.     

Detection filter design for LPV systems—a geometric approach

This paper investigates fault detection and isolation of linear parameter-varying (LPV) systems by using parameter-varying (C,A)-invariant subspace and parameter-varying unobservability subspaces. The so called “detection filter” approach, formulated as the fundamental problem of residual generation (FPRG) for linear time-invariant (LTI) systems, is extended for a class of LPV systems. The question of stability is addressed in the terms of Lyapunov quadratic stability by using linear matrix inequalities. The results are applied to the model of a generic small commercial aircraft.     

Decentralized overlapping control of a formation of unmanned aerial vehicles

Decentralized overlapping feedback laws are designed for a formation of unmanned aerial vehicles. The dynamic model of the formation with an information structure constraint in which each vehicle, except the leader, only detects the vehicle directly in front of it, is treated as an interconnected system with overlapping subsystems. Using the mathematical framework of the inclusion principle, the interconnected system is expanded into a higher dimensional space in which the subsystems appear to be disjoint. Then, at each subsystem, a static state feedback controller is designed to robustly stabilize the perturbed nominal dynamics of the subsystem. The design procedure is based on the application of convex optimization tools involving linear matrix inequalities. As a final step, the decentralized controllers are contracted back to the original interconnected system for implementation.     

Distributed intelligence for multi-camera visual surveillance

Latest advances in hardware technology and state of the art of computer vision and artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. The paper proposes a multi-agent architecture for the understanding of scene dynamics merging the information streamed by multiple cameras. A typical application would be the monitoring of a secure site, or any visual surveillance application deploying a network of cameras. Modular software (the agents) within such architecture controls the different components of the system and incrementally builds a model of the scene by merging the information gathered over extended periods of time. The role of distributed artificial intelligence composed of separate and autonomous modules is justified by the need for scalable designs capable of co-operating to infer an optimal interpretation of the scene. Decentralizing intelligence means creating more robust and reliable sources of interpretation, but also allows easy maintenance and updating of the system. Results are presented to support the choice of a distributed architecture, and to prove that scene interpretation can be incrementally and efficiently built by modular software.     

Multiple costs based decision making with back-propagation neural networks

The current research investigates a single cost for cost-sensitive neural networks (CNN) for decision making. This may not be feasible for real cost-sensitive decisions which involve multiple costs. We propose to modify the existing model, the traditional back-propagation neural networks (TNN), by extending the back-propagation error equation for multiple cost decisions. In this multiple-cost extension, all costs are normalized to be in the same interval (i.e. between 0 and 1) as the error estimation generated in the TNN. A comparative analysis of accuracy dependent on three outcomes for constant costs was performed: (1) TNN and CNN with one constant cost (CNN-1C), (2) TNN and CNN with two constant costs (CNN-2C), and (3) CNN-1C and CNN-2C. A similar analysis for accuracy was also made for non-constant costs; (1) TNN and CNN with one non-constant cost (CNN-1NC), (2) TNN and CNN with two non-constant costs (CNN-2NC), and (3) CNN-1NC and CNN-2NC. Furthermore, we compared the misclassification cost for CNNs for both constant and non-constant costs (CNN-1C vs. CNN-2C and CNN-1NC vs. CNN-2NC). Our findings demonstrate that there is a competitive behavior between the accuracy and misclassification cost in the proposed CNN model. To obtain a higher accuracy and lower misclassification cost, our results suggest merging all constant cost matrices into one constant cost matrix for decision making. For multiple non-constant cost matrices, our results suggest maintaining separate matrices to enhance the accuracy and reduce the misclassification cost.     

Extracting the optimal dimensionality for local tensor discriminant analysis

Supervised dimensionality reduction with tensor representation has attracted great interest in recent years. It has been successfully applied to problems with tensor data, such as image and video recognition tasks. However, in the tensor-based methods, how to select the suitable dimensions is a very important problem. Since the number of possible dimension combinations exponentially increases with respect to the order of tensor, manually selecting the suitable dimensions becomes an impossible task in the case of high-order tensor. In this paper, we aim at solving this important problem and propose an algorithm to extract the optimal dimensionality for local tensor discriminant analysis. Experimental results on a toy example and real-world data validate the effectiveness of the proposed method.     

Improved diagnosis using enhanced fault dominance

Defect diagnosis can benefit from fault dominance relations to reduce the set of defect candidate sites. This paper presents new fault dominance collapsing operators that further reduce the set of candidates considered during the initial phase of diagnosis. In contrast to existing dominance-based methods which operate on pairs of faults, the proposed method operates on sets of faults. Fault-related entities are generated to guide the diagnosis process. The proposed collapsing operators can be used to accelerate effect-cause diagnosis. Experimental results demonstrate that the proposed method achieves a higher collapsing ratio than existing methods.     

WIPO Panels’ interpretation of the Uniform Dispute Resolution Policy (UDRP) three-prong test

The Uniform Dispute Resolution Policy (UDRP) is a unique process that resolves domain name disputes effectively and inexpensively. This paper, through an analysis of the UDRP three-prong test, revealed that even though the UDRP affords a great degree of discretion to the WIPO Panels deciding any given case, there is some consistency and predictability inherent in the UDRP process.     

Europe should stop taxing innovation

After nearly 48 years of failure to create the EU patent, language issues and the design of a centralised patent-litigation court still dominate headlines. But behind these issues there are high financial stakes and control power to play for. The recent EU Council deal on an ‘enhanced’ European patent system does not solve the above problems, and has not eliminated lingering governance issues. The risk for Europe is that a final patent agreement might be reached that does not cure the system of its major ills, and thus does not bring about any significant improvement for those that need it most: entrepreneurs and innovative companies starting out on the innovation process. The creation of an effective single EU patent requires (i) English-only post-grant translation, (ii) the end of nationally granted patents, (iii) phasing-out of the current ‘European patent’, (iv) lower fees for young innovative companies, and (v) a radical shake-up of the governance of the European Patent Office.     

Synthesis of crystalline metal nitride and metal carbide nanostructures by sol–gel chemistry

Attention toward nanosized metal nitrides and carbides is rapidly increasing thanks to their chemical characteristics that make them as valid and sustainable alternatives to noble metals in catalysis and to air-sensitive metals or oxides for applications under harsh conditions. They are mostly used as bulk phase or micron sized powders, due to an intrinsic difficulty to synthesize them as nanoparticles in a systematic and scalable fashion. However, nanosized metal nitrides and carbides could exhibit improved performances, e.g. in catalysis due to a higher surface area, and can be shaped more easily than corresponding larger grains for further specific applications. Recently, sol–gel chemistry has closed this gap and now enables the simple, cheap, and sustainable production of metal nitride and carbide nanoparticles.In the present review we give an overview on recent sol–gel based pathways for the synthesis of metal nitride and carbide nanoparticles, believing that a better knowledge of the potentialities of these still hardly touched materials stimulates research interest and applications.