Intelligently tuned wavelet parameters for GPS/INS error estimation

This paper presents a new algorithm for de-noising global positioning system (GPS) and inertial navigation system (INS) data and estimates the INS error using wavelet multi-resolution analysis algorithm (WMRA)-based genetic algorithm (GA) with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of th...     

Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition

The automatic recognition of dialogue act is a task of crucial importance for the processing of natural language dialogue at discourse level. It is also one of the most challenging problems as most often the dialogue act is not expressed directly in speaker’s utterance. In this paper, a new cue-based model for dialogue act recognition is presented. The model is, essentially, a dynamic Bayesian network induced from manually annotated dialogue corpus via dynamic Bayesian machine learning algorithms. Furthermore, the dynamic Bayesian network’s random variables are constituted from sets of lexical cues selected automatically by means of a variable length genetic algorithm, developed specifically for this purpose. To evaluate the proposed approaches of design, three stages of experiments have been conducted. In the initial stage, the dynamic Bayesian network model is constructed using sets of lexical cues selected manually from the dialogue corpus. The model is evaluated against two previously proposed models and the results confirm the potentiality of dynamic Bayesian networks for dialogue act recognition. In the second stage, the developed variable length genetic algorithm is used to select different sets of lexical cues to constitute the dynamic Bayesian networks’ random variables. The developed approach is evaluated against some of the previously used ranking approaches and the results provide experimental evidences on its ability to avoid the drawbacks of the ranking approaches. In the third stage, the dynamic Bayesian networks model is constructed using random variables constituted from the sets of lexical cues generated in the second stage and the results confirm the effectiveness of the proposed approaches for designing dialogue act recognition model.     

A new dual wing harmonium model for document retrieval

A new dual wing harmonium model that integrates term frequency features and term connection features into a low dimensional semantic space without increase of computation load is proposed for the application of document retrieval. Terms and vectorized graph connectionists are extracted from the graph representation of document by employing weighted feature extraction method. We then develop a new dual wing harmonium model projecting these multiple features into low dimensional latent topics with different probability distributions assumption. Contrastive divergence algorithm is used for efficient learning and inference. We perform extensive experimental verification, and the comparative results suggest that the proposed method is accurate and computationally efficient for document retrieval.     

A new document representation using term frequency and vectorized graph connectionists with application to document retrieval

This paper presents a new document representation with vectorized multiple features including term frequency and term-connection-frequency. A document is represented by undirected and directed graph, respectively. Then terms and vectorized graph connectionists are extracted from the graphs by employing several feature extraction methods. This hybrid document feature representation more accurately reflects the underlying semantics that are difficult to achieve from the currently used term histograms, and it facilitates the matching of complex graph. In application level, we develop a document retrieval system based on self-organizing map (SOM) to speed up the retrieval process. We perform extensive experimental verification, and the results suggest that the proposed method is computationally efficient and accurate for document retrieval.     

Replica Placement Strategies in Data Grid

Replication is a technique used in Data Grid environments that helps to reduce access latency and network bandwidth utilization. Replication also increases data availability thereby enhancing system reliability. The research addresses the problem of replication in Data Grid environment by investigating a set of highly decentralized dynamic replica placement algorithms. Replica placement algorithms are based on heuristics that consider both network latency and user requests to select the best candidate sites to place replicas. Due to dynamic nature of Grid, the candidate site holds replicas currently may not be the best sites to fetch replicas in subsequent periods. Therefore, a replica maintenance algorithm is proposed to relocate replicas to different sites if the performance metric degrades significantly. The study of our replica placement algorithms is carried out using a model of the EU Data Grid Testbed 1 [Bell et al. Comput. Appl., 17(4), 2003] sites and their associated network geometry. We validate our replica placement algorithms with total file transfer times, the number of local file accesses, and the number of remote file accesses.     

Pressure measurements in laboratory-scale blast wave flow fields

The present study examines the effects that temporal and spatial averagings due to finite size and finite response time of pressure transducers have on the pressure measurements in blast wave flow fields generated by milligram charges of silver azide. In such applications, the characteristic time and length scales of the physical process are of the same order of magnitude as the temporal and spatial characteristics of the transducer. The measured pressure values will then be spatially and temporally averaged, and important parameters for the assessment of blast effects may not be properly represented in the measured trace. In this study, face-on and side-on pressure transducer setups are considered. In the experiments, face-on and side-on readings at the same distance from the charge as well as time-resolved optical visualization of the whole flow field are obtained simultaneously for the same explosive event. The procedure of data extraction from the experimental pressure traces is revisited and discussed in detail. In the numerical modeling part of the study, numerical blast flow fields are generated using an Euler flow solver. A numerical pressure transducer model is developed to qualitatively simulate the averaging effects. The experimental and numerical data show that the results of pressure measurements in experiments with small charges must be used with great caution. The effective averaging of the pressure signal may lead to a significant underestimation of blast wave intensities. The side-on setup is especially prone to this effect. The face-on setup provides results close to those obtained from optical records only if the pressure transducer is sufficiently remote from the charge.     

D.c. and a.c. electrical properties of vacuum evaporated thin SiO/GeO2 films

The d.c. and a.c. electrical properties were studied for various compositions of SiO/GeO₂ co-evaporated thin films carrying aluminium electrodes, in the temperature range 193–413 K. A.c. measurements were made over the frequency range 2x10²–10⁶Hz. The value of the d.c. activation energy was found to decrease with increasing GeO₂ content in the SiO. In the region of high applied field (above 10⁶ Vm^–1, the conduction mechanism is governed by Schottky emission at the blocking contact. The a.c. electrical conductivity, (), varies with frequency according to the relation () ^s, where the exponent s was found to be dependent on temperature and frequency. The a.c. conduction at low temperature was due to an electronic hopping process. The number of localized sites was estimated from the a.c. measurements for different compositions of SiO/GeO₂ using the models proposed by Elliott and by Pollak, and the values are compared. The Elliott model satisfactorily accounts for the observed a.c. electrical results. A correlation was found between activation energy, optical band gap, conductivity and number of localized sites for the various compositions of SiO/GeO₂ films. The relative dielectric constant, _r, and loss factor, tan , were found to increase with the increase of GeO₂ content in the films.     

Multiple expert classification: a new methodology for parallel decision fusion

Abstract. A new parallel hybrid decision fusion methodology is proposed. It is demonstrated that existing parallel multiple expert decision combination approaches can be divided into two broad categories based on the implicit decision emphasis implemented. The first category consists of methods implementing computationally intensive decision frameworks incorporating a priori information about the target task domain and the reliability of the participating experts, while the second category encompasses approaches implementing group consensus without assigning any importance to the reliability of the experts and ignoring other contextual information. The methodology proposed in this paper is a hybridisation of these two approaches and has shown significant performance enhancements in terms of higher overall recognition rates along with lower substitution rates. Detailed analysis using two different databases supports this claim. Received January 19, 1999 / Revised March 20, 2000     

Implications to stormwater management as a result of lot scale rainwater tank systems: a case study in Western Sydney, Australia

Rainwater tanks are increasingly adopted in Australia to reduce potable water demand and are perceived to reduce the volume of stormwater discharge from developments. This paper investigates the water balance of rainwater tanks, in particular the possible impacts these tanks could have in controlling the stormwater discharge volume. The study collected water quantity data from two sites in the Hawkesbury City Council area, New South Wales, Australia and utilised the collected data in a simple water balance model to assess the effectiveness of rainwater tanks in reducing the stormwater discharge volume. The results indicate that a significant reduction in discharge volume from a lot scale development can be achieved if the rainwater tank is connected to multiple end-uses, but is minimal when using irrigation alone. In addition, the commonly used volumetric runoff coefficient of 0.9 was found to over-estimate the runoff from the roof areas and to thereby under-estimate the available volume within the rainwater tanks for retention or detention. Also, sole reliance on the water in the rainwater tanks can make the users aware of their water use pattern and water availability, resulting in significant reductions in water use as the supply dwindles, through self-imposed water restrictions.     

Vision Based UAV Attitude Estimation: Progress and Insights

Unmanned aerial vehicles (UAVs) are increasingly replacing manned systems in situations that are dangerous, remote, or difficult for manned aircraft to access. Its control tasks are empowered by computer vision technology. Visual sensors are robustly used for stabilization as primary or at least secondary sensors. Hence, UAV stabilization by attitude estimation from visual sensors is a very active research area. Vision based techniques are proving their effectiveness and robustness in handling this problem. In this work a comprehensive review of UAV vision based attitude estimation approaches is covered, starting from horizon based methods and passing by vanishing points, optical flow, and stereoscopic based techniques. A novel segmentation approach for UAV attitude estimation based on polarization is proposed. Our future insightes for attitude estimation from uncalibrated catadioptric sensors are also discussed.