Generation and assessment of accuracy of Digital Elevation Model by using Pcigeomatica (10.3)

This study aims to assess the accuracy of Digital Elevation Model (DEM) which is generated by using Toutin’s model. Thus, Toutin’s model was run by using OrthoEngineSE of PCI Geomatics 10.3.Thealong-track stereoimages of Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) sensor with 15 m resolution were used to produce DEM on an area with low and near Mean Sea Level (MSL) elevation in Johor Malaysia. Despite the satisfactory pre-processing results the visual assessment of the DEM generated from Toutin’s model showed that the DEM contained many outliers and incorrect values. The failure of Toutin’s model may mostly be due to the inaccuracy and insufficiency of ASTER ephemeris data for low terrains as well as huge water body in the stereo images.

A two-stage item recommendation method using probabilistic ranking with reconstructed tensor model

In a tag-based recommender system, the multi-dimensional correlation should be modeled effectively for finding quality recommendations. Recently, few researchers have used tensor models in recommendation to represent and analyze latent relationships inherent in multi-dimensions data. A common approach is to build the tensor model, decompose it and, then, directly use the reconstructed tensor to generate the recommendation based on the maximum values of tensor elements. In order to improve the accuracy and scalability, we propose an implementation of the -mode block-striped (matrix) product for scalable tensor reconstruction and probabilistically ranking the candidate items generated from the reconstructed tensor. With testing on real-world datasets, we demonstrate that the proposed method outperforms the benchmarking methods in terms of recommendation accuracy and scalability.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Frequency-motivated observer design for the prediction of parametric roll resonance control applications in marine systems

This work deals with estimators for predicting when parametric roll resonance is going to occur in surface vessels. The roll angle of the vessel is modeled as a second-order linear oscillatory system with unknown parameters. Several algorithms are used to estimate the parameters and eigenvalues of the system based on data gathered experimentally on a 1:45 scale model of a tanker. Based on the estimated eigenvalues, the system predicts whether or not parametric roll occurred. A prediction accuracy of 100% is achieved for regular waves, and up to 87.5% for irregular waves.

Ranking based clustering for social event detection

The problem of clustering a large document collection is not only challenged by the number of documents and the number of dimensions, but it is also affected by the number and sizes of the clusters. Traditional clustering methods fail to scale when they need to generate a large number of clusters. Furthermore, when the clusters size in the solution is heterogeneous, i.e. some of the clusters are large in size, the similarity measures tend to degrade. A ranking based clustering method is proposed to deal with these issues in the context of the Social Event Detection task. Ranking scores are used to select a small number of most relevant clusters in order to compare and place a document. Additionally,instead of conventional cluster centroids, cluster patches are proposed to represent clusters, that are hubs-like set of documents. Text, temporal, spatial and visual content information collected from the social event images is utilized in calculating similarity. Results show that these strategies allow us to have a balance between performance and accuracy of the clustering solution gained by the clustering method.

Geochemistry-based coral palaeoclimate studies and the potential of ‘non-traditional’ (non-massive Porites) corals : recent developments and future progression

Understanding the natural variability of the Earth's climate system and accurately identifying potential anthropogenic influences requires long term, geographically distributed records of key climate indicators, such as temperature and precipitation that extend prior to the last 400. years of the Holocene. Reef corals provide an excellent source of high resolution climate records, and importantly represent the tropical marine environment where palaeoclimate data are urgently required. Recent decades have seen significant improvement in our understanding of coral biomineralisation, the associated uptake of geochemical proxies and methods of identifying and understanding the effects of both early and late, post depositional diagenetic alteration. These processes all have significant implications for interpreting geochemical proxies relevant to palaeoclimatic reconstructions. This paper reviews the current 'state of the art' in terms of coral based palaeoclimate reconstructions and highlights a key remaining problem. The majority of coral based palaeoclimate research has been derived from massive colonies of Porites. However, massive Porites are not globally abundant and may not provide material of a particular age of interest in those regions where they are present. Therefore, there is great potential for alternate coral genera to act as complimentary climate archives. While it remains critical to consider five key factors - vital effects, differential growth morphologies, geochemical heterogeneity in the skeletal ultrastructure, transfer equation selection and diagenetic screening of skeletal material - in order to allow the highest level of accuracy in coral palaeoclimate reconstructions, it is also important to develop alternate taxa for palaeoclimate studies in regions where Porites colonies are absent or rare. Currently as many as nine genera other than Porites have proven at least limited utility in palaeothermometry, most of which are found in the Atlantic/Caribbean region where massive Porites do not exist. Even branching taxa such as Acropora have significant potential to preserve environmental archives. Increasing this capability will greatly expand the number of potential geochemical archives available for longer term temporal records of palaeoclimate.

A new approach to spatial data interpolation using higher-order statistics

Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function (CPDF) is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures.

Age-related changes in perception of movement in driving scenes

Purpose Age-related changes in motion sensitivity have been found to relate to reductions in various indices of driving performance and safety. The aim of this study was to investigate the basis of this relationship in terms of determining which aspects of motion perception are most relevant to driving. Methods Participants included 61 regular drivers (age range 22–87 years). Visual performance was measured binocularly. Measures included visual acuity, contrast sensitivity and motion sensitivity assessed using four different approaches: (1) threshold minimum drift rate for a drifting Gabor patch, (2) Dmin from a random dot display, (3) threshold coherence from a random dot display, and (4) threshold drift rate for a second-order (contrast modulated) sinusoidal grating. Participants then completed the Hazard Perception Test (HPT) in which they were required to identify moving hazards in videos of real driving scenes, and also a Direction of Heading task (DOH) in which they identified deviations from normal lane keeping in brief videos of driving filmed from the interior of a vehicle. Results In bivariate correlation analyses, all motion sensitivity measures significantly declined with age. Motion coherence thresholds, and minimum drift rate threshold for the first-order stimulus (Gabor patch) both significantly predicted HPT performance even after controlling for age, visual acuity and contrast sensitivity. Bootstrap mediation analysis showed that individual differences in DOH accuracy partly explained these relationships, where those individuals with poorer motion sensitivity on the coherence and Gabor tests showed decreased ability to perceive deviations in motion in the driving videos, which related in turn to their ability to detect the moving hazards. Conclusions The ability to detect subtle movements in the driving environment (as determined by the DOH task) may be an important contributor to effective hazard perception, and is associated with age, and an individuals' performance on tests of motion sensitivity. The locus of the processing deficits appears to lie in first-order, rather than second-order motion pathways.

Gold nanospikes based microsensor as a highly accurate mercury emission monitoring system

Anthropogenic elemental mercury (Hg0) emission is a serious worldwide environmental problem due to the extreme toxicity of the heavy metal to humans, plants and wildlife. Development of an accurate and cheap microsensor based online monitoring system which can be integrated as part of Hg0 removal and control processes in industry is still a major challenge. Here, we demonstrate that forming Au nanospike structures directly onto the electrodes of a quartz crystal microbalance (QCM) using a novel electrochemical route results in a self-regenerating, highly robust, stable, sensitive and selective Hg0 vapor sensor. The data from a 127 day continuous test performed in the presence of volatile organic compounds and high humidity levels, showed that the sensor with an electrodeposted sensitive layer had 260% higher response magnitude, 3.4 times lower detection limit (,22 mg/m3 or ,2.46 ppbv) and higher accuracy (98% Vs 35%) over a Au control based QCM (unmodified) when exposed to a Hg0 vapor concentration of 10.55 mg/m3 at 1016C. Statistical analysis of the long term data showed that the nano-engineered Hg0 sorption sites on the developed Au nanospikes sensitive layer play a critical role in the enhanced sensitivity and selectivity of the developed sensor towards Hg0 vapor.